Archives: Jobs

Job Description

We are seeking an experienced and results-driven AI Program Manager to lead and oversee enterprise Artificial Intelligence initiatives across multiple business units. The ideal candidate will be responsible for managing AI programs from strategy and planning through deployment and optimization, ensuring alignment with business objectives and organizational goals.

The AI Program Manager will collaborate with executives, data scientists, AI engineers, product managers, cybersecurity teams, compliance stakeholders, and business leaders to drive successful AI adoption, governance, and innovation. This role requires strong leadership, project management expertise, and a solid understanding of AI technologies, machine learning, and emerging industry trends.

Key Responsibilities

• Lead the planning, execution, and delivery of enterprise AI programs and initiatives.
• Develop AI program roadmaps, timelines, budgets, and resource plans.
• Coordinate cross-functional teams including AI Engineers, Data Scientists, Product Managers, and Business Stakeholders.
• Monitor AI project performance, risks, dependencies, and milestones.
• Ensure AI solutions align with organizational goals and business requirements.
• Establish governance frameworks for responsible AI development and deployment.
• Support AI compliance, security, privacy, and risk management initiatives.
• Manage vendor relationships and third-party AI technology providers.
• Track AI adoption metrics, business outcomes, and return on investment (ROI).
• Present program updates, progress reports, and strategic recommendations to executive leadership.
• Identify opportunities for process improvement and AI-driven innovation.
• Stay informed about emerging AI technologies, regulations, and industry best practices.

Required Qualifications

• Bachelor's degree in Business Administration, Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or a related field.
• 5+ years of experience in Program Management, Project Management, Technology Management, or AI-related initiatives.
• Experience managing large-scale technology or AI transformation projects.
• Strong understanding of Artificial Intelligence, Machine Learning, Generative AI, and Data Analytics concepts.
• Experience working with Agile, Scrum, or hybrid project management methodologies.
• Excellent communication, leadership, and stakeholder management skills.
• Ability to manage multiple projects simultaneously in a fast-paced environment.
• Strong analytical and problem-solving capabilities.

Preferred Qualifications

• Master's degree in Business Administration (MBA), Data Science, Artificial Intelligence, or a related field.
• Experience with AI Governance, Responsible AI, or AI Risk Management frameworks.
• Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with MLOps, Data Engineering, and AI deployment processes.
• Experience in regulated industries such as healthcare, finance, government, or technology.

Preferred Certifications

• Project Management Professional (PMP)
• Certified ScrumMaster (CSM)
• PMI Agile Certified Practitioner (PMI-ACP)
• Microsoft Certified: Azure AI Engineer Associate
• AWS Certified Machine Learning – Specialty
• Google Professional Machine Learning Engineer

Technical Skills

• AI Program Management
• Artificial Intelligence
• Generative AI
• Machine Learning
• Project Management
• Agile & Scrum
• AI Governance
• AI Risk Management
• Data Analytics
• Business Strategy
• Stakeholder Management
• Budget Planning
• Cloud Computing (AWS, Azure, GCP)
• MLOps
• Change Management

Benefits

• Competitive salary and annual performance bonuses
• Medical, Dental, and Vision Insurance
• Remote and Hybrid Work Opportunities
• Paid Time Off and Company Holidays
• Professional Development and Certification Reimbursement
• Employee Stock Purchase Programs (where applicable)
• Career Advancement Opportunities

Employment Type

• Full-Time
• Remote
• Hybrid
• Contract (Project-Based)

Job Description

We are seeking a highly motivated Cyber Threat Hunter to proactively identify, investigate, and mitigate advanced cyber threats across the organization's environment. The ideal candidate will possess strong analytical skills, experience in threat hunting, incident response, and cybersecurity operations, and the ability to uncover malicious activities that evade traditional security controls.

As a Cyber Threat Hunter, you will work closely with Security Operations Center (SOC), Incident Response, Threat Intelligence, and Security Engineering teams to detect sophisticated threats, improve detection capabilities, and strengthen the organization's overall security posture.

Key Responsibilities

• Proactively hunt for advanced threats and suspicious activities across enterprise networks, endpoints, cloud environments, and applications.
• Analyze security events, logs, network traffic, and endpoint telemetry to identify indicators of compromise (IOCs).
• Conduct investigations into potential security incidents and emerging cyber threats.
• Develop and execute threat hunting hypotheses based on threat intelligence and attack trends.
• Perform malware analysis and behavioral analysis to understand adversary tactics, techniques, and procedures (TTPs).
• Collaborate with SOC analysts and incident response teams to contain and remediate threats.
• Create detection rules, use cases, and threat-hunting playbooks.
• Utilize SIEM, EDR, XDR, and threat intelligence platforms to improve detection capabilities.
• Document findings and provide actionable recommendations to security leadership.
• Stay informed about evolving cyber threats, nation-state actors, ransomware groups, and industry attack trends.

Required Qualifications

• Bachelor's degree in Cybersecurity, Information Security, Computer Science, Information Technology, or a related field.
• 3+ years of experience in Threat Hunting, Incident Response, SOC Operations, or Cybersecurity Analysis.
• Experience with SIEM platforms such as Splunk, Microsoft Sentinel, QRadar, or LogRhythm.
• Strong knowledge of network protocols, operating systems, and cybersecurity principles.
• Experience with Endpoint Detection and Response (EDR) tools.
• Understanding of MITRE ATT&CK Framework and Cyber Kill Chain methodologies.
• Knowledge of malware analysis and threat intelligence techniques.
• Strong analytical, investigative, and problem-solving skills.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Experience in cloud security (AWS, Azure, or Google Cloud).
• Knowledge of scripting languages such as Python, PowerShell, or Bash.
• Experience with digital forensics investigations.
• Familiarity with threat intelligence platforms and OSINT techniques.
• Experience hunting advanced persistent threats (APTs) and ransomware attacks.

Preferred Certifications

• CompTIA Security+
• CompTIA CySA+
• GIAC Certified Incident Handler (GCIH)
• GIAC Certified Intrusion Analyst (GCIA)
• GIAC Cyber Threat Intelligence (GCTI)
• Certified Ethical Hacker (CEH)
• Certified Information Systems Security Professional (CISSP)

Technical Skills

• Threat Hunting
• Threat Intelligence
• Incident Response
• Digital Forensics
• Malware Analysis
• SIEM Platforms
• Splunk
• Microsoft Sentinel
• QRadar
• CrowdStrike Falcon
• Microsoft Defender
• Carbon Black
• MITRE ATT&CK
• Network Security
• Endpoint Security
• Python
• PowerShell
• Linux
• Windows Security

Benefits

• Competitive salary and annual performance bonus
• Medical, dental, and vision insurance
• Remote and hybrid work options
• Paid time off and company holidays
• Professional certification reimbursement
• Career advancement and training opportunities

Job Description

We are seeking a highly analytical and detail-oriented Quantitative Data Scientist to join our growing analytics team. In this role, you will leverage advanced statistical techniques, machine learning models, and quantitative methodologies to solve complex business challenges and support strategic decision-making. The ideal candidate will possess strong mathematical and programming skills, experience working with large datasets, and the ability to translate analytical findings into actionable business insights.

You will collaborate closely with data engineers, business stakeholders, product teams, and leadership to develop predictive models, optimize business processes, and drive innovation through data-driven solutions.

Key Responsibilities

• Develop and implement quantitative models to analyze complex datasets and identify trends, patterns, and opportunities.
• Design predictive and forecasting models using machine learning and statistical techniques.
• Perform advanced statistical analysis, hypothesis testing, and experimental design.
• Analyze structured and unstructured data from multiple sources.
• Create risk assessment, customer behavior, and performance optimization models.
• Build and maintain data pipelines and analytical workflows.
• Present findings and recommendations to technical and non-technical stakeholders.
• Collaborate with cross-functional teams to define business objectives and analytical strategies.
• Monitor and improve model performance, accuracy, and scalability.
• Stay updated on emerging technologies, statistical methods, and industry best practices.

Required Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field.
• 3+ years of experience in data science, quantitative analytics, or a similar role.
• Strong proficiency in Python, R, SQL, or similar programming languages.
• Experience with machine learning frameworks such as Scikit-Learn, TensorFlow, or PyTorch.
• Strong understanding of statistical modeling, probability, and data analysis techniques.
• Experience with data visualization tools such as Tableau, Power BI, or Matplotlib.
• Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.
• Excellent problem-solving and communication skills.

Preferred Qualifications

• PhD in a quantitative discipline.
• Experience in finance, healthcare, technology, insurance, or consulting industries.
• Familiarity with big data technologies such as Spark, Hadoop, or Databricks.
• Experience with optimization algorithms and time-series forecasting.
• Professional certifications in Data Science, Machine Learning, or Cloud Technologies.

Benefits

• Competitive salary and performance bonuses.
• Comprehensive medical, dental, and vision insurance.
• 401(k) retirement plan with company matching.
• Flexible work arrangements and remote work opportunities.
• Paid time off and company holidays.
• Professional development and certification support.
• Career growth opportunities within a rapidly expanding organization.

Skills

• Statistical Modeling
• Machine Learning
• Predictive Analytics
• Data Mining
• Python
• R
• SQL
• Data Visualization
• Forecasting
• Risk Analytics
• Probability Theory
• Business Intelligence
• Cloud Computing
• Experimental Design
• Quantitative Research

Job Summary

We are seeking a highly skilled AI Operations (AIOps) Engineer to join our Technology Operations team. The ideal candidate will leverage Artificial Intelligence (AI), Machine Learning (ML), and automation technologies to enhance IT operations, improve system reliability, and proactively identify and resolve incidents across enterprise environments.

The AIOps Engineer will work closely with DevOps, Site Reliability Engineering (SRE), Cloud Engineering, and Cybersecurity teams to implement intelligent monitoring, predictive analytics, and automated remediation solutions that optimize operational efficiency and reduce downtime.

Job Description

The AIOps Engineer is responsible for designing, implementing, and managing AI-driven operational solutions that automate monitoring, incident management, anomaly detection, and performance optimization. This role combines expertise in machine learning, cloud computing, data analytics, and IT operations to build intelligent systems capable of analyzing large volumes of operational data and providing actionable insights.

The position requires strong technical expertise in cloud platforms, observability tools, automation frameworks, and machine learning techniques to improve infrastructure resilience and operational performance.

Key Responsibilities

AI-Driven Monitoring & Observability

• Develop and maintain AI-powered monitoring solutions for applications, infrastructure, and cloud environments.
• Implement anomaly detection and predictive analytics models to identify operational issues before they impact users.
• Analyze logs, metrics, and events from enterprise systems to generate actionable insights.
• Build dashboards and visualizations to monitor system health and performance.

Incident Management & Automation

• Design intelligent incident detection and response workflows.
• Develop automated remediation solutions for common operational issues.
• Reduce Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) incidents.
• Integrate AIOps platforms with IT Service Management (ITSM) systems.

Machine Learning & Data Analytics

• Build machine learning models for predictive maintenance and capacity planning.
• Analyze historical operational data to identify trends and optimize system performance.
• Develop data pipelines to process and analyze high-volume telemetry data.
• Continuously improve model accuracy and operational effectiveness.

Cloud & Infrastructure Optimization

• Monitor and optimize cloud resources and application performance.
• Implement automation solutions for cloud provisioning and configuration management.
• Improve system reliability, scalability, and availability.
• Support multi-cloud environments and distributed systems.

Collaboration & Cross-Functional Support

• Partner with DevOps, SRE, Infrastructure, and Security teams to improve operational resilience.
• Participate in major incident investigations and root cause analysis.
• Provide recommendations for performance optimization and operational improvements.
• Document AIOps architectures, workflows, and best practices.

Governance & Continuous Improvement

• Establish AIOps standards and operational procedures.
• Ensure data quality and integrity across monitoring platforms.
• Monitor the effectiveness of AI-driven operational processes.
• Identify opportunities for automation and operational efficiency improvements.

Required Qualifications

• Bachelor's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, or a related field.
• 3–7+ years of experience in IT Operations, DevOps, Site Reliability Engineering, Cloud Engineering, or AIOps.
• Strong understanding of machine learning concepts and operational analytics.
• Experience with cloud computing, monitoring tools, and automation technologies.
• Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

• Master's degree in Artificial Intelligence, Data Science, Computer Science, or Information Systems.
• Experience with large-scale distributed systems and enterprise cloud environments.
• Industry certifications such as:
  • AWS Certified Solutions Architect
  • Microsoft Azure Administrator Associate
  • Google Professional Cloud Engineer
  • Certified Kubernetes Administrator (CKA)
  • ITIL Foundation Certification

Technical Skills

Programming & Scripting

• Python
• SQL
• Bash
• PowerShell

Cloud Platforms

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud Platform (GCP)

Monitoring & Observability Tools

• Datadog
• Dynatrace
• Splunk
• Prometheus
• Grafana
• New Relic

DevOps & Automation

• Docker
• Kubernetes
• Terraform
• Jenkins
• GitHub Actions
• CI/CD Pipelines

Machine Learning & Analytics

• Scikit-learn
• TensorFlow
• Pandas
• NumPy
• Predictive Analytics
• Anomaly Detection

Key Performance Indicators (KPIs)

• Reduction in Mean Time to Detect (MTTD)
• Reduction in Mean Time to Resolve (MTTR)
• Infrastructure Availability and Uptime
• Incident Prediction Accuracy
• Percentage of Automated Incident Resolution
• Reduction in Operational Costs
• System Performance and Reliability Improvements

Job Summary

We are seeking a highly analytical and detail-oriented Cyber Risk Quantification Analyst to join our Cyber Security and Risk Management team. The ideal candidate will evaluate and quantify cyber risks by leveraging data analytics, financial modeling, and cybersecurity frameworks to estimate the potential business impact of cyber threats. This role partners with cybersecurity, finance, technology, and executive leadership teams to enable data-driven risk management and strategic decision-making.

Job Description

The Cyber Risk Quantification Analyst is responsible for translating technical cyber risks into measurable business and financial impacts. This role develops quantitative risk models, performs scenario analyses, and provides actionable insights that support cybersecurity investments, regulatory compliance, and enterprise risk management initiatives.

The position requires expertise in cybersecurity principles, data analytics, risk assessment methodologies, and financial analysis to communicate cyber risks in business terms.

Key Responsibilities

Cyber Risk Assessment & Quantification

• Identify and assess cyber threats, vulnerabilities, and business risks across the organization.
• Quantify potential financial losses resulting from cyber incidents such as ransomware, data breaches, insider threats, and system outages.
• Develop risk models to estimate likelihood, impact, and exposure to cyber events.
• Maintain enterprise cyber risk registers and risk metrics.

Data Analytics & Modeling

• Analyze large datasets related to security incidents, threat intelligence, vulnerabilities, and operational risks.
• Develop statistical and predictive models to measure cyber risk exposure.
• Conduct scenario analysis and simulations to evaluate potential cyber events.
• Build risk scoring methodologies and dashboards for executive reporting.

Business Impact Analysis

• Estimate operational, financial, legal, and reputational impacts of cyber incidents.
• Collaborate with business units to identify critical assets and dependencies.
• Support cyber insurance evaluations and financial risk assessments.
• Develop key risk indicators (KRIs) and risk tolerance metrics.

Reporting & Executive Communication

• Translate technical security findings into business-oriented reports and recommendations.
• Prepare cyber risk presentations for senior leadership and risk committees.
• Develop dashboards and metrics to communicate enterprise cyber risk posture.
• Support strategic cybersecurity investment decisions through quantitative analysis.

Governance, Risk & Compliance (GRC)

• Align cyber risk assessments with industry frameworks and standards.
• Support internal and external audit activities.
• Ensure compliance with cybersecurity and risk management policies.
• Contribute to enterprise risk management and governance initiatives.

Cross-Functional Collaboration

• Partner with cybersecurity operations, IT, finance, legal, and compliance teams.
• Support incident response teams during major security events.
• Participate in risk workshops and tabletop exercises.
• Provide recommendations for risk mitigation strategies and control improvements.

Required Qualifications

• Bachelor's degree in Cyber Security, Information Systems, Data Science, Computer Science, Statistics, Finance, Mathematics, or a related field.
• 3–7+ years of experience in cyber risk management, cybersecurity analytics, enterprise risk management, or quantitative analysis.
• Strong understanding of cybersecurity principles, threat landscapes, and risk assessment methodologies.
• Experience in statistical analysis, financial modeling, and data analytics.
• Excellent analytical, communication, and problem-solving skills.

Preferred Qualifications

• Master's degree in Cyber Security, Risk Management, Data Analytics, or Business Administration.
• Professional certifications such as:
  • Certified Information Systems Security Professional (CISSP)
  • Certified Information Security Manager (CISM)
  • Certified in Risk and Information Systems Control (CRISC)
  • FAIR (Factor Analysis of Information Risk) Certification
  • Certified Information Systems Auditor (CISA)

Technical Skills

Programming & Analytics

• Python
• SQL
• R (Preferred)
• Excel and Financial Modeling

Data Visualization

• Power BI
• Tableau
• Looker

Cybersecurity Tools & Platforms

• SIEM Platforms (Splunk, Microsoft Sentinel, QRadar)
• Vulnerability Management Platforms
• GRC Platforms
• Risk Management Dashboards

Frameworks & Methodologies

• FAIR (Factor Analysis of Information Risk)
• NIST Cybersecurity Framework
• ISO 27001
• Enterprise Risk Management (ERM)
• Quantitative Risk Modeling

Key Performance Indicators (KPIs)

• Accuracy of cyber risk quantification models
• Enterprise cyber risk exposure reduction
• Timeliness of risk assessments and reporting
• Effectiveness of risk mitigation recommendations
• Executive adoption of risk metrics and dashboards
• Cyber risk reporting quality and completeness
• Support for strategic cybersecurity investment decisions

Job Summary

We are seeking a highly motivated Customer Analytics Scientist to join our growing Data Science team. The ideal candidate will leverage advanced analytics, machine learning, and statistical techniques to understand customer behavior, improve customer experiences, and drive business growth. This role will partner closely with marketing, product, sales, and leadership teams to transform customer data into actionable insights and strategic recommendations.

Key Responsibilities

Customer Behavior Analysis

• Analyze customer journeys, purchasing patterns, and engagement metrics.
• Identify customer segments and behavioral trends using data-driven techniques.
• Develop customer lifetime value (CLV) models and retention strategies.

Predictive Modeling

• Build predictive models for customer acquisition, churn prediction, and upsell opportunities.
• Develop recommendation engines and personalization algorithms.
• Create forecasting models to improve customer engagement and revenue generation.

Experimentation & Optimization

• Design and execute A/B tests and experiments to evaluate marketing campaigns and product features.
• Measure campaign effectiveness and identify opportunities for optimization.
• Monitor model performance and continuously improve predictive accuracy.

Data Visualization & Reporting

• Create dashboards and interactive reports using business intelligence tools.
• Present findings and recommendations to technical and non-technical stakeholders.
• Translate complex analytical insights into business actions.

Cross-Functional Collaboration

• Partner with marketing, product, sales, and engineering teams to solve customer-related business problems.
• Support strategic initiatives by providing analytical recommendations and customer insights.
• Contribute to data-driven decision-making across the organization.

Data Management & Governance

• Ensure data quality, consistency, and integrity across customer data sources.
• Implement best practices for data privacy and security compliance.
• Document methodologies, models, and analytical processes.

Required Qualifications

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field.
• 3–6+ years of experience in customer analytics, data science, or business analytics.
• Strong proficiency in Python, SQL, and statistical analysis.
• Experience with machine learning techniques such as classification, clustering, regression, and recommendation systems.
• Hands-on experience with Power BI, Tableau, or Looker.
• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Master's degree in Data Science, Analytics, Statistics, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Knowledge of marketing analytics, CRM systems, and customer segmentation methodologies.
• Familiarity with big data technologies such as Spark or Databricks.

Technical Skills

• Programming: Python, SQL, R
• Machine Learning: Scikit-learn, XGBoost, TensorFlow
• Data Visualization: Power BI, Tableau, Looker
• Databases: SQL Server, PostgreSQL, Snowflake
• Cloud Platforms: AWS, Azure, Google Cloud Platform
• Analytics Techniques: Customer Segmentation, Churn Prediction, Customer Lifetime Value (CLV), Recommendation Systems, A/B Testing

Key Performance Indicators (KPIs)

• Customer Retention Rate
• Customer Lifetime Value (CLV)
• Churn Prediction Accuracy
• Campaign Conversion Rate
• Revenue Growth from Personalization Initiatives
• Customer Engagement Metrics
• Recommendation Model Performance

Job Summary

IBM is seeking an AI Governance & Ethics Specialist to develop and implement responsible AI frameworks, ensure compliance with emerging AI regulations, and establish governance practices for the ethical development and deployment of Artificial Intelligence solutions. The ideal candidate will work closely with data scientists, legal teams, security professionals, and business stakeholders to ensure AI systems are transparent, fair, secure, and compliant with global standards.

Job Description

As an AI Governance & Ethics Specialist, you will:

• Develop and implement Responsible AI policies and governance frameworks.
• Establish standards for ethical AI development and deployment.
• Conduct AI risk assessments and identify potential biases and fairness issues.
• Ensure compliance with AI regulations, data privacy laws, and industry standards.
• Develop AI accountability, transparency, and explainability programs.
• Collaborate with legal, cybersecurity, and engineering teams on AI governance initiatives.
• Create AI monitoring and audit processes for enterprise AI systems.
• Design governance strategies for Generative AI and Large Language Model (LLM) applications.
• Provide recommendations on AI ethics, security, and risk management practices.

Key Responsibilities

AI Governance

• Develop enterprise AI governance frameworks.
• Define AI policies, standards, and operating procedures.
• Create governance models for AI lifecycle management.

AI Risk Management

• Conduct AI risk assessments and impact analyses.
• Identify bias, fairness, and explainability concerns.
• Implement controls to reduce AI-related risks.

Compliance & Regulations

• Ensure adherence to global AI regulations and privacy requirements.
• Support AI compliance audits and assessments.
• Develop AI governance documentation and reporting.

Responsible AI Implementation

• Establish principles for ethical and trustworthy AI.
• Define AI transparency and accountability requirements.
• Create monitoring and evaluation mechanisms for AI systems.

Cross-Functional Collaboration

• Work with data scientists and AI engineers to implement governance controls.
• Collaborate with legal and cybersecurity teams on compliance initiatives.
• Present governance strategies and recommendations to executive leadership.

Required Technical Skills

• Artificial Intelligence Fundamentals
• Responsible AI & Ethical AI Principles
• AI Governance Frameworks
• AI Risk Management
• Generative AI & Large Language Models (LLMs)
• Data Privacy & Data Governance
• AI Explainability and Fairness Techniques
• Security and Compliance Fundamentals
• Python and Data Analytics (Preferred)
• Cloud Platforms (AWS, Azure, Google Cloud)

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Systems, Law, or a related field.
• 3–7+ years of experience in AI, Data Governance, Risk Management, Compliance, or Cybersecurity.
• Knowledge of Generative AI, LLMs, and enterprise AI deployment.
• Understanding of AI ethics, privacy, and governance best practices.
• Excellent communication, analytical, and stakeholder management skills.

Key Skills

✅ Responsible AI & Ethical AI
✅ AI Governance & Compliance
✅ AI Risk Management
✅ Generative AI & Large Language Models (LLMs)
✅ Data Privacy & Governance
✅ AI Explainability & Fairness
✅ Enterprise Risk Management
✅ Security & Regulatory Compliance
✅ Cloud & AI Platforms
✅ Strategic Communication & Leadership

Benefits

✅ Annual Performance Bonus
✅ Health, Dental, and Vision Insurance
✅ Hybrid/Remote Work Opportunities
✅ Professional Certifications and Training Programs
✅ Leadership Development Programs
✅ Career Advancement Opportunities

Job Summary

Deloitte is seeking a motivated Power BI Developer to design, develop, and maintain business intelligence dashboards and reporting solutions. The ideal candidate will transform complex data into actionable insights, support business decision-making, and collaborate with cross-functional teams to deliver high-quality analytics solutions.

Job Description

As a Power BI Developer, you will:

• Develop interactive dashboards and reports using Microsoft Power BI.
• Gather and analyze business requirements for reporting solutions.
• Create and optimize data models and relationships.
• Build ETL processes for data extraction and transformation.
• Design KPI dashboards and executive reports.
• Integrate data from databases, APIs, and cloud platforms.
• Ensure data accuracy, security, and governance standards.
• Troubleshoot and optimize report performance.
• Work closely with analysts, developers, and business stakeholders to deliver data-driven solutions.

Key Responsibilities

Dashboard Development

• Build interactive and visually appealing dashboards.
• Create drill-down and drill-through reporting capabilities.
• Develop self-service reporting solutions.

Data Modeling

• Design star and snowflake schema data models.
• Create calculated columns and measures using DAX.
• Develop relationships between multiple datasets.

Data Integration

• Connect Power BI with SQL Server, Excel, SharePoint, and APIs.
• Build automated data refresh pipelines.
• Perform data cleansing and transformation using Power Query.

Reporting & Analytics

• Develop KPIs and performance scorecards.
• Generate business reports and trend analysis.
• Provide actionable insights through data visualization.

Collaboration

• Gather business requirements from stakeholders.
• Translate business needs into technical solutions.
• Present findings and recommendations to management teams.

Required Technical Skills

• Power BI Desktop & Power BI Service
• DAX (Data Analysis Expressions)
• Power Query (M Language)
• SQL & T-SQL
• Microsoft Excel (Advanced)
• Data Modeling & Data Warehousing
• Data Visualization & Dashboard Design
• Basic Python (Preferred)
• Azure Fundamentals (Preferred)
• Strong Communication & Problem-Solving Skills

Preferred Qualifications

• Bachelor's degree in Computer Science, Information Technology, Data Science, Business Analytics, or a related field.
• 0–2 years of experience in Power BI, Business Intelligence, or Data Analytics.
• Knowledge of SQL databases and reporting tools.
• Understanding of ETL concepts and data warehousing.
• Microsoft Power BI certification (PL-300) is preferred.

Benefits

✅ Annual Performance Bonus
✅ Medical, Dental, and Vision Insurance
✅ Hybrid Work Environment
✅ Paid Training and Certification Programs
✅ 401(k) Retirement Plan
✅ Career Growth Opportunities

Job Summary

IBM is seeking an Applied Data Scientist specializing in Generative AI to design, develop, and deploy enterprise-grade AI solutions powered by Large Language Models (LLMs). The role focuses on transforming business challenges into scalable AI products using machine learning, natural language processing, and Generative AI technologies. IBM's data and analytics teams work on responsible AI adoption and enterprise AI solutions across cloud environments.

Job Description

As an Applied Data Scientist (Generative AI), you will:

• Develop and fine-tune Large Language Models (LLMs) for enterprise applications.
• Build intelligent AI assistants, chatbots, and knowledge management systems.
• Design and implement Retrieval-Augmented Generation (RAG) architectures.
• Create end-to-end machine learning and Generative AI pipelines.
• Work with structured and unstructured data to generate actionable insights.
• Deploy scalable AI solutions on cloud platforms.
• Collaborate with engineering, product, and business teams to deliver AI-driven innovations.
• Evaluate and optimize model performance, accuracy, and reliability.
• Implement responsible AI practices, governance, and model monitoring frameworks. Generative AI, LLMs, and production-grade AI deployment have become core requirements in modern data science roles.

Key Responsibilities

Generative AI Development

• Fine-tune foundation models and LLMs.
• Implement prompt engineering strategies.
• Develop conversational AI and AI agents.

Data Science & Machine Learning

• Build predictive and prescriptive analytics models.
• Perform data preprocessing and feature engineering.
• Conduct experimentation and model evaluation.

Enterprise AI Solutions

• Design RAG systems using vector databases.
• Integrate AI solutions with business applications.
• Develop scalable AI APIs and microservices.

Deployment & MLOps

• Deploy models using Docker and Kubernetes.
• Implement CI/CD pipelines for AI applications.
• Monitor model performance and automate retraining.

Collaboration & Leadership

• Partner with stakeholders to identify AI opportunities.
• Translate business requirements into technical solutions.
• Present findings and recommendations to leadership teams.

Required Technical Skills

• Programming: Python, SQL, PySpark
• Machine Learning: Scikit-learn, XGBoost, TensorFlow, PyTorch
• Generative AI: LLMs, Prompt Engineering, Fine-Tuning, AI Agents
• Frameworks: LangChain, LlamaIndex, Hugging Face
• RAG Systems: Vector Databases, Semantic Search, Embeddings
• Cloud Platforms: AWS, Azure, Google Cloud Platform
• Big Data: Apache Spark, Databricks, Hadoop
• MLOps: MLflow, Docker, Kubernetes, CI/CD
• Databases: PostgreSQL, MongoDB, Pinecone, ChromaDB

Preferred Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Artificial Intelligence, or a related field.
• 3–7+ years of experience in Data Science, Machine Learning, or AI Engineering.
• Hands-on experience with Generative AI applications and LLM deployment.
• Strong understanding of NLP, deep learning, and distributed computing.
• Excellent communication and problem-solving skills.

Benefits

✅ Competitive Salary + Annual Bonus
✅ Stock Options and Performance Incentives
✅ Health, Dental, and Vision Insurance
✅ Hybrid/Remote Work Opportunities
✅ Learning and Certification Programs
✅ Generative AI and Cloud Training

Job Summary

We are seeking a highly skilled Deep Learning Engineer to develop, train, optimize, and deploy state-of-the-art deep learning models for real-world AI applications. The ideal candidate will have expertise in neural networks, computer vision, natural language processing (NLP), generative AI, and large-scale machine learning systems.

You will work with AI researchers, data scientists, software engineers, and product teams to build next-generation AI solutions that power intelligent products and services across industries.

Key Responsibilities

Deep Learning Model Development

• Design, develop, and optimize deep neural network architectures.
• Build and train machine learning and deep learning models using large-scale datasets.
• Develop solutions for Computer Vision, NLP, Speech AI, and Generative AI applications.
• Fine-tune foundation models and Large Language Models (LLMs).

Model Training & Optimization

• Perform hyperparameter tuning and model optimization.
• Improve model accuracy, scalability, and efficiency.
• Implement distributed training strategies for large-scale models.
• Conduct model evaluation and validation.

Production Deployment

• Deploy deep learning models into production environments.
• Develop scalable inference pipelines.
• Monitor model performance and retrain models as required.
• Implement MLOps best practices.

Research & Innovation

• Stay updated on the latest AI research and advancements.
• Experiment with emerging architectures and techniques.
• Collaborate with research teams to translate innovations into production solutions.
• Publish technical findings and contribute to AI knowledge sharing.

Collaboration

• Work closely with data engineering teams to build data pipelines.
• Partner with product teams to define AI requirements.
• Support AI integration across enterprise applications.
• Mentor junior engineers and contribute to technical leadership.

Required Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Mathematics, or a related field.

Experience

• 3+ years of experience in Deep Learning, Machine Learning, or AI Engineering.
• Experience developing and deploying production-grade AI solutions.
• Strong understanding of neural networks and advanced AI algorithms.

Technical Skills

Programming Languages

• Python
• C++
• SQL

Deep Learning Frameworks

• PyTorch
• TensorFlow
• Keras
• JAX

AI & Machine Learning

• Deep Neural Networks (DNN)
• CNNs (Convolutional Neural Networks)
• RNNs & LSTMs
• Transformers
• Generative AI
• Reinforcement Learning
• Transfer Learning

Specialized Areas

• Computer Vision
• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Multimodal AI
• Speech Recognition

Cloud & Infrastructure

• AWS
• Azure
• Google Cloud Platform (GCP)
• Docker
• Kubernetes

Data & MLOps

• Apache Spark
• MLflow
• Kubeflow
• Airflow
• Git/GitHub

Preferred Certifications

• NVIDIA Deep Learning Institute Certifications
• Google Cloud Professional Machine Learning Engineer
• Microsoft Azure AI Engineer Associate
• Amazon Web Services Machine Learning Specialty

Preferred Skills

• Model Optimization & Quantization
• Distributed Training
• CUDA Programming
• GPU Acceleration
• AI Model Deployment
• MLOps & Model Monitoring
• Research and Innovation
• Problem Solving & Communication Skills

Benefits

• Competitive Base Salary
• Annual Performance Bonus
• RSU/Stock Grants
• Medical, Dental & Vision Insurance
• Flexible Hybrid Work Model
• Learning & Development Programs
• Career Advancement Opportunities

Job Summary

We are seeking a detail-oriented Third-Party Risk Analyst to evaluate, monitor, and manage risks associated with vendors, suppliers, business partners, and external service providers. The ideal candidate will support the organization's Third-Party Risk Management (TPRM) program by conducting risk assessments, ensuring regulatory compliance, and identifying potential cybersecurity, operational, financial, and compliance risks across the vendor ecosystem.

This role collaborates with Procurement, Information Security, Legal, Compliance, Privacy, and Business teams to strengthen vendor governance and minimize organizational risk exposure.

Key Responsibilities

Vendor Risk Assessment

• Conduct comprehensive third-party risk assessments for new and existing vendors.
• Evaluate vendor security controls, compliance posture, and operational resilience.
• Review security questionnaires, SOC reports, audit reports, and risk documentation.
• Perform inherent and residual risk analysis.

Risk Monitoring & Due Diligence

• Monitor vendor risk throughout the vendor lifecycle.
• Identify and assess cybersecurity, operational, legal, privacy, and financial risks.
• Track remediation plans and vendor corrective actions.
• Maintain vendor risk registers and assessment records.

Compliance & Regulatory Support

• Ensure compliance with industry regulations and standards.
• Support audits related to third-party risk management.
• Assist in developing vendor governance policies and procedures.
• Evaluate vendor compliance with contractual security requirements.

Reporting & Stakeholder Management

• Prepare risk assessment reports and executive summaries.
• Present findings and recommendations to management.
• Collaborate with procurement and business units during vendor onboarding.
• Support risk committees and governance meetings.

Continuous Improvement

• Improve third-party risk assessment methodologies.
• Automate vendor risk management processes where possible.
• Monitor emerging risks and industry best practices.
• Contribute to enterprise risk management initiatives.

Required Qualifications

Education

• Bachelor's degree in Cybersecurity, Information Technology, Information Security, Risk Management, Business Administration, or related field.

Experience

• 3+ years of experience in Third-Party Risk Management (TPRM), Vendor Risk Management, Cybersecurity Risk, Compliance, or Information Security.
• Experience reviewing vendor security documentation and audit reports.
• Familiarity with enterprise risk management frameworks.

Technical Skills

• Third-Party Risk Management (TPRM)
• Vendor Risk Assessment
• Cybersecurity Risk Analysis
• Risk Registers & Risk Scoring
• Security Questionnaires
• Vendor Due Diligence
• Contract Risk Review
• Data Privacy Assessments
• Risk Reporting & Governance

Frameworks & Standards

• NIST Cybersecurity Framework
• NIST 800-53
• ISO 27001
• SOC 2
• PCI-DSS
• HIPAA
• GDPR
• CCPA

Tools

• Archer
• OneTrust
• ServiceNow GRC
• LogicGate
• RSA Archer
• Jira
• Microsoft Excel & Power BI

Preferred Certifications

• ISC2 CISSP
• ISACA CISM
• ISACA CRISC
• Shared Assessments Certified Third-Party Risk Professional (CTPRP)
• CompTIA Security+

Preferred Skills

• Strong analytical and risk assessment capabilities.
• Excellent written and verbal communication skills.
• Ability to manage multiple vendor assessments simultaneously.
• Experience working with cross-functional teams.
• Knowledge of cybersecurity controls and cloud security practices.
• Strong stakeholder management and presentation skills.

Job Summary

We are seeking a highly skilled Multimodal AI Engineer to develop next-generation AI systems capable of understanding, processing, and generating content across multiple modalities, including text, images, audio, video, and structured data. The ideal candidate will work on cutting-edge foundation models, vision-language models (VLMs), and multimodal Generative AI applications that power enterprise and consumer experiences.

Job Description

As a Multimodal AI Engineer, you will design, train, fine-tune, and deploy advanced multimodal AI models that integrate computer vision, natural language processing, speech technologies, and generative AI. You will collaborate with research scientists, machine learning engineers, product teams, and cloud architects to build scalable AI solutions for real-world applications.

The role requires expertise in deep learning frameworks, multimodal model architectures, MLOps, cloud computing, and AI model optimization. You will help drive innovation in areas such as AI assistants, intelligent search, content generation, document understanding, video analytics, and autonomous systems.

Key Responsibilities

• Design and develop multimodal AI applications using text, image, audio, and video data.
• Train, fine-tune, and optimize Vision-Language Models (VLMs) and Large Language Models (LLMs).
• Build AI systems that combine natural language understanding with computer vision capabilities.
• Develop Retrieval-Augmented Generation (RAG) pipelines for multimodal content.
• Integrate AI models into enterprise products and cloud platforms.
• Create scalable data pipelines for training and inference workflows.
• Evaluate model performance, accuracy, robustness, and safety.
• Optimize AI models for latency, scalability, and production deployment.
• Collaborate with AI researchers to implement emerging architectures and techniques.
• Ensure responsible AI practices, fairness, security, and compliance standards.
• Monitor production AI systems and continuously improve model performance.
• Stay updated on the latest advancements in Generative AI and multimodal foundation models.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• 4+ years of experience in AI, Machine Learning, Deep Learning, or Data Science.
• Experience building and deploying machine learning models in production environments.
• Strong understanding of multimodal AI systems and foundation models.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities.

Required Technical Skills

Artificial Intelligence & Machine Learning

• Deep Learning
• Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Vision-Language Models (VLMs)
• Reinforcement Learning Fundamentals

Computer Vision

• Image Classification
• Object Detection
• Image Captioning
• Visual Question Answering (VQA)
• OCR and Document Intelligence

Natural Language Processing

• NLP
• Transformers
• Prompt Engineering
• Text Generation
• Semantic Search
• RAG Frameworks

Programming & Frameworks

• Python
• PyTorch
• TensorFlow
• Hugging Face Transformers
• OpenCV
• CUDA

Data & MLOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• Apache Spark
• Airflow

Cloud Platforms

• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)

Preferred Qualifications

• Experience with multimodal foundation models such as GPT-4o, LLaVA, CLIP, Gemini, or similar architectures.
• Knowledge of AI model compression and optimization techniques.
• Experience working with distributed AI training environments.
• Familiarity with vector databases and AI agent frameworks.
• Understanding of Responsible AI and AI governance principles.

Preferred Certifications

• AWS Certified Machine Learning Specialty
• Microsoft Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• NVIDIA Deep Learning Institute Certifications

Tools & Technologies

• Python
• PyTorch
• TensorFlow
• Hugging Face
• LangChain
• LlamaIndex
• OpenCV
• Docker
• Kubernetes
• MLflow
• Databricks
• Snowflake
• Pinecone
• Weaviate
• AWS
• Azure
• GCP

Success Metrics

• Successful deployment of multimodal AI applications.
• Improved model accuracy and user experience.
• Reduced inference latency and operational costs.
• Scalable AI infrastructure and deployment pipelines.
• Increased adoption of AI-powered products and services.
• Compliance with security, privacy, and responsible AI standards.

Job Summary

We are seeking an experienced Zero Trust Security Architect to lead the design, implementation, and optimization of enterprise-wide Zero Trust security strategies. This role is responsible for securing users, devices, applications, workloads, and data across on-premises, cloud, and hybrid environments. The ideal candidate will have deep expertise in security architecture, identity and access management, network segmentation, and cloud security.

Key Responsibilities

• Design and implement enterprise-wide Zero Trust Architecture (ZTA) aligned with business and security objectives.
• Develop security frameworks based on the principle of "Never Trust, Always Verify."
• Architect identity-centric security controls for workforce, partners, and third-party users.
• Lead micro-segmentation initiatives across cloud and on-premises environments.
• Implement continuous authentication and authorization mechanisms.
• Design secure access solutions for remote and hybrid workforces.
• Evaluate existing security infrastructure and recommend Zero Trust transformation strategies.
• Collaborate with Security Operations, Cloud, Network, and Infrastructure teams.
• Develop security reference architectures, standards, and best practices.
• Conduct security risk assessments and architecture reviews.
• Ensure compliance with NIST, CISA, ISO 27001, SOC 2, and other security frameworks.
• Support incident response and threat mitigation initiatives.
• Present architectural recommendations to executive leadership and stakeholders.

Required Qualifications

• Bachelor's degree in Cybersecurity, Information Technology, Computer Science, or a related field.
• 8+ years of experience in Cybersecurity, Security Engineering, or Security Architecture.
• 3+ years of experience designing or implementing Zero Trust security models.
• Strong understanding of enterprise security architecture principles.
• Experience securing cloud, hybrid, and multi-cloud environments.
• Excellent communication, leadership, and stakeholder management skills.

Required Technical Skills

Zero Trust & Security Architecture

• Zero Trust Architecture (ZTA)
• Security Architecture Design
• Enterprise Security Frameworks
• Security Risk Management
• Security Controls Assessment

Identity & Access Management (IAM)

• Single Sign-On (SSO)
• Multi-Factor Authentication (MFA)
• Privileged Access Management (PAM)
• Identity Governance & Administration (IGA)
• Conditional Access Policies

Cloud Security

• AWS Security
• Microsoft Azure Security
• Google Cloud Security
• Cloud Access Security Broker (CASB)
• Secure Access Service Edge (SASE)

Network Security

• Micro-Segmentation
• Software-Defined Perimeter (SDP)
• Secure Remote Access
• Network Access Control (NAC)
• Firewall Architecture

Security Operations

• SIEM Solutions
• Endpoint Detection & Response (EDR)
• Extended Detection & Response (XDR)
• Security Monitoring
• Threat Intelligence

Preferred Certifications

• Certified Information Systems Security Professional (CISSP)
• Certified Cloud Security Professional (CCSP)
• Certified Information Security Manager (CISM)
• GIAC Security Certifications (GIAC)
• Microsoft Cybersecurity Architect Expert
• AWS Security Specialty
• Zero Trust Professional Certifications

Tools & Technologies

• Microsoft Entra ID (Azure AD)
• Okta
• CyberArk
• Palo Alto Networks
• Zscaler
• CrowdStrike
• Microsoft Defender
• Splunk
• AWS
• Azure
• Google Cloud Platform (GCP)
• Terraform
• Kubernetes

Success Metrics

• Successful implementation of Zero Trust initiatives.
• Reduction in unauthorized access risks.
• Improved identity and access governance.
• Enhanced cloud and network security posture.
• Compliance audit readiness and reduced security gaps.
• Faster detection and response to cyber threats.

Benefits

• Competitive Base Salary
• Annual Performance Bonus
• Health, Dental, and Vision Insurance
• Remote/Hybrid Work Flexibility
• Paid Time Off (PTO)
• Professional Certifications & Training Reimbursement
• Employee Wellness Programs
• Leadership Development Opportunities

Job Summary

We are seeking a highly analytical and business-oriented Data Science Product Analyst to transform data into actionable insights that drive product growth, customer engagement, and business performance. The ideal candidate will work closely with Product Managers, Data Scientists, Engineers, and Business Leaders to evaluate product performance, conduct experiments, and recommend data-driven improvements.

Key Responsibilities

• Analyze user behavior and product performance using large-scale datasets.
• Design, execute, and evaluate A/B tests and product experiments.
• Develop dashboards, reports, and KPI tracking frameworks.
• Identify growth opportunities through data exploration and predictive analytics.
• Collaborate with Product Managers to define product success metrics.
• Generate actionable insights to improve customer acquisition, retention, and engagement.
• Build statistical models to forecast product performance and user behavior.
• Monitor key business metrics and identify trends, anomalies, and opportunities.
• Present findings and recommendations to stakeholders and executive leadership.
• Support product roadmap decisions with data-driven analysis.

Required Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field.
• 3+ years of experience in Product Analytics, Data Science, Business Analytics, or a related role.
• Strong proficiency in SQL for data extraction and analysis.
• Experience with Python or R for statistical analysis and modeling.
• Knowledge of experimentation methodologies and A/B testing.
• Strong analytical, problem-solving, and communication skills.
• Experience working with large datasets and cloud-based analytics platforms.

Preferred Skills

• Product Analytics
• Statistical Modeling
• A/B Testing & Experimentation
• SQL
• Python (Pandas, NumPy, Scikit-learn)
• Tableau, Power BI, or Looker
• Data Visualization
• Customer Journey Analytics
• Predictive Analytics
• Machine Learning Fundamentals
• Google Analytics or Adobe Analytics
• Data Warehousing (Snowflake, BigQuery, Redshift)

Tools & Technologies

• SQL
• Python / R
• Tableau
• Power BI
• Looker
• Snowflake
• Google BigQuery
• AWS / Azure / GCP
• Jupyter Notebook
• Excel

Success Metrics

• Improved product adoption and user engagement.
• Increased customer retention and conversion rates.
• Accurate forecasting and business insights.
• Effective experimentation and data-driven product decisions.
• Enhanced reporting and KPI visibility across teams.

Benefits

• Competitive Salary
• Annual Performance Bonus
• Health, Dental, and Vision Insurance
• Remote/Hybrid Work Flexibility
• Paid Time Off (PTO)
• Professional Development & Certifications
• Career Growth Opportunities
• Employee Wellness Programs