Top Data & Analytics Jobs

Lead quantitative analyses to design, implement, and evaluate Medicare and Medicaid payment models. Query large claims/enrollment datasets, build payment calculations, financial and quality metrics, and apply ML/econometric/predictive models. Mentor staff, write reproducible code and documentation, develop client relationships, perform rapid ad-hoc analyses, and help integrate AI into technical workflows.

Lead design and implementation of enterprise fraud prevention strategy and decisioning engines for loan products. Use data analytics and machine learning to assess fraud and identity risk, recommend verification standards, set KPIs, report to senior leadership, and lead/mentor a team to automate and improve fraud prevention while maintaining a frictionless customer experience.

Build reusable, analytics-ready telematics data products on Azure Databricks. Design canonical datasets, standardize metrics and business logic, optimize performance and cost, enable self-service analytics, and support AI/ML use cases for enterprise telemetry and connected-vehicle data.

Lead control and risk management for Data & Analytics products and platforms. Ensure adherence to the CORE program, embed AI/ML and data governance controls, manage Risk Assessment Structures, support control testing and issue remediation, develop control documentation, partner with Legal/Compliance/OpRisk/Audit, and support regulatory responses while leading and advising stakeholders.

Lead Data Architect responsible for defining and evolving data architecture and standards across 100+ applications, driving adoption of strategic data lake platforms, designing target-state architectures and data exchange patterns, mentoring junior architects, and ensuring governance, data quality, and cloud-based database best practices.

Build and operate scalable data pipelines and trustworthy data models supporting Layla’s AI-powered travel products. Own data quality, reliability, observability, experimentation, and model-evaluation foundations. Enable production AI/ML systems through feature and data availability, evaluation datasets, and monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights, establish engineering best practices, and mentor other engineers.

Design, harden, and scale Snowflake and AWS data platforms, build dimensional data models and resilient Airflow pipelines, optimize complex SQL, develop dbt semantic layers, deploy ML models in AWS, integrate AI/LLMs into workflows, and build data APIs (FastAPI/Flask) to enable enterprise analytics and operational ROI.

Lead architecture for data, reporting, and analytics product family: set target-state designs, guide technology evaluation and AI-enabled integrations, produce and review production code, enforce governance, resiliency, security, and auditability, and provide technical leadership across teams and vendors.

Leads HPE’s global Sales decision sciences organization of approximately 100 professionals across business intelligence, reporting, data architecture, advanced analytics, and AI. The role establishes sales measurement frameworks, executive reporting, predictive and prescriptive decision support, reporting automation, data governance, and a trusted Sales Data Lake. It partners with IT, data governance, finance, marketing, and sales leadership to improve forecasting, seller productivity, business performance, and management decision-making while building a globally integrated analytics organization.

Builds and maintains production-grade ETL/ELT pipelines across legacy on-premise systems and AWS/Azure cloud platforms, centralizing data in Snowflake for analytics, machine learning, feature stores, and generative AI. Develops vectorization and embedding pipelines, optimizes Snowflake performance and cloud costs, manages cross-platform orchestration, and implements data quality and observability frameworks. Partners with IT, security, MLOps, data science, and infrastructure teams to deliver scalable, governed data solutions.

Leads enterprise business intelligence and data analytics for a U.S. Combatant Command program. Develops Power BI architectures, dashboards, data models, pipelines, KPIs, and reporting products for leadership decision-making. Establishes data governance, quality, provenance, metadata, access controls, and authoritative data sources across classified and unclassified environments. Translates stakeholder decision needs into analytic requirements, evaluates knowledge-management initiatives, supports AI and machine-learning capabilities, documents solutions, trains users, and mentors analysts.

Supports data and analytics projects by designing ETL processes, developing data pipelines, integrating and validating data, and improving data quality. Creates Power BI reports and dashboards, troubleshoots data and storage issues, and helps enhance data warehouse and analytics solutions. The intern also explores AI-enabled tools, cloud platforms, and modern data engineering practices while collaborating with business partners.

Build and lead business and operational analytics for semiconductor products, establishing performance-management systems, KPIs, NPI health analytics, predictive models, AI-enabled workflows, and scalable operating mechanisms. Translate cross-functional data into executive insights, resource and investment decisions, risk identification, and measurable financial impact. Build the analytics organization, define its operating model, drive transformation, and scale operational intelligence across the Power Products Group.

Integrate operational data into enterprise data warehouses; design, optimize, and support SSIS ETL pipelines; develop SSRS and Power BI reporting solutions and reusable data models; deliver data migrations and self-service extracts. Design production AI/ML solutions using Python, OCR, AWS Bedrock, and Claude, including multi-agent LLM implementations. Partner with stakeholders, lead code reviews, mentor developers, and support production BI deployments.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Leads Autodesk’s enterprise GTM revenue analytics foundation, including KPI governance, semantic data models, reporting standards, dashboards, and executive insights. Partners with data, systems, finance, sales, marketing, and customer success teams to improve revenue decision-making. Defines AI-enabled analytics use cases and roadmap while ensuring scalable, secure, governed solutions. Serves as a technical and analytical leader, influencing cross-functional priorities and mentoring team members.

Design and develop scalable data solutions and tooling to enable AI and advanced analytics across Azure hardware and software stacks. Identify opportunities for AI-driven impact, build data platforms and visualizations, and collaborate with engineers, data scientists, and program managers to improve efficiency, quality, and operational insights.

Build and maintain data reporting, visualization, analytics, and automation solutions for the Office of the Chief Legal Officer. Consult with stakeholders to define requirements, map data flows and processes, troubleshoot issues, support governance and security, and present analytical findings. Coach associates on self-service analytics and data literacy while collaborating on data profiling, modeling, quality, and standardized reporting practices.

Develop and validate commercial credit risk models (rating, loss forecasting, stress testing, pricing). Collaborate with business, data infrastructure, and governance teams to deploy, monitor, and document cloud-based model solutions and derive business insights from large datasets.

Work within Morgan Stanley's Business Solutions Group to deliver consulting engagements, program delivery, and data-driven analytics. Manage cross-functional projects, define problems, design data platforms, build dashboards and automation, analyze financial and operational data, and support ML/LLM initiatives including RAG and agentic workflows. Engage stakeholders, track risks and metrics, and prepare presentations for senior management.

Lead design and delivery of Snowflake-based data solutions, data models, ETL pipelines and data warehouse applications. Provide architecture direction, drive technology transformation and POCs, evangelize modern data architecture, collaborate with global stakeholders and Agile teams, and apply advanced analytics (ML/AI/GenAI) while ensuring data security and PII management.

Develop interactive dashboards, visual analytics platforms, and automated reports for SATCOM, network operations, engineering, and operational data. Integrate machine learning model outputs into user-facing interfaces, including predictions, confidence intervals, explainability, and performance metrics. Establish visualization standards, guide technical architecture, collaborate across engineering and data science teams, mentor junior staff, and present findings to customers and senior leaders.

Analyze credit, fraud, pricing, and portfolio performance using internal and external data. Cleanse and validate data, monitor predictive models and risk strategies, develop statistical models, identify trends and data-quality issues, and communicate actionable insights to leadership. Collaborate cross-functionally with product, engineering, compliance, and enterprise risk teams to support strategic decisions, improve profitability, and manage B2B credit risk.

Implement metadata governance frameworks for structured and unstructured data in cloud environments. Track data lineage and synchronize metadata across data catalogs, ESRI Kubernetes, and enterprise repositories. Use OpenLineage, Apache Atlas, and related tools to manage metadata and lineage. Enforce DoD metadata policies and NSG standards for geospatial datasets while advising clients and guiding data science teams.

Own customer experience and delivery metrics, dashboards, and reporting across Support, Professional Services, Customer Success, and Renewals. Analyze customer, operational, usage, and financial data to identify risks, trends, bottlenecks, and renewal threats. Partner with leaders to define KPIs, translate business questions into recommendations, validate data quality, automate reporting, and build self-service analytics. Collaborate with AI teams to measure the impact of AI-driven tools.