Top Data & Analytics Jobs

Lead a team of data scientists to enhance infrastructure through data science, focusing on capacity planning, performance optimization, and customer experience.

Lead a team of data scientists to optimize Databricks infrastructure using data science for capacity planning, reliability, and performance improvements.

Leads enterprise analytics, automation, and value discovery initiatives by integrating data, building Power BI dashboards and models, monitoring exceptions, and identifying revenue, margin, operational, inventory, pricing, and compliance opportunities. Partners cross-functionally to prioritize and measure improvements, conduct root-cause analysis, maintain data governance, and evaluate AI-enabled solutions. The role requires advanced analytics expertise, executive communication, stakeholder management, and oversight of multiple enterprise initiatives.

Design and implement mathematical optimization engines and digital‑twin simulation models integrated with predictive ML to optimize production scheduling, sequencing, and resource allocation for aerospace manufacturing facilities. Validate models, analyze large manufacturing datasets, collaborate with ML and plant teams, and translate results for stakeholders to improve throughput and reduce WIP.

Leads a multidisciplinary team of marketing analysts and researchers, combining marketing analytics, market research, advanced analytics, and performance insights. Oversees data infrastructure, SQL development, pipelines, APIs, ELT/ETL tools, Snowflake, and Tableau dashboards. Guides predictive modeling, segmentation, research methodology, AI experimentation, data governance, and client presentations. Develops team capabilities, workflows, and standards while translating complex data into actionable marketing and business recommendations.

Build predictive data pipelines, machine learning models, dashboards, and automated decision tools for portfolio strategy. Analyze product performance, costs, monetization, competitive intelligence, customer behavior, and feature opportunities. Govern metrics and data quality using SQL while enabling self-service AI and natural-language data access. Support roadmap prioritization, ROI assessment, initiative tracking, and executive decision-making through advanced analytics and financial modeling.

Builds data models, dashboards, metrics, automated Python and SQL pipelines, lightweight analytics applications, and machine learning solutions for Safety, Quality, Mission Assurance, and Manufacturing Operations. Partners with business and technical stakeholders to gather requirements, deliver self-service reporting, support predictive insights, and lead cross-functional data modeling while maintaining data quality and documentation standards.

Analyze communication data from multiple sources to identify trends, patterns, correlations, and performance gaps. Build automated data pipelines, connectors, dashboards, reports, data models, scorecards, and visualizations using Microsoft Power Platform, Python, SQL, and related tools. Translate communication objectives into measurable KPIs, advise Marines and senior leaders on findings, brief stakeholders, and support strategic engagement and process improvement decisions.

Direct advanced analytics for a marketing agency, leading a team that develops statistical models, marketing mix models, attribution algorithms, campaign tests, and ROI analyses. Manage client engagements, mentor analysts, improve analytical methodologies, productionize models, and collaborate with business intelligence and data engineering teams. Translate complex technical findings into actionable business recommendations while supporting media optimization and marketing effectiveness initiatives.

Develop and maintain Palantir Foundry data pipelines, transformations, analytical workflows, dashboards, and user-facing applications. Analyze and validate data from multiple sources, translate stakeholder requirements into solutions, troubleshoot issues, and support data modeling, testing, documentation, and deployment. Apply Python, SQL, JavaScript or TypeScript, ETL/ELT, cloud, low-code, and UX/UI practices while collaborating with technical teams and government clients.

Design and implement end-to-end AI features, scalable data pipelines, ETL/ELT workflows, Generative AI and agentic systems, ML and BI solutions, and MLOps/LLMOps practices across AWS, Azure, and GCP. Apply RAG, embeddings, vector databases, security governance, evaluation, reliability, and guardrails. Own technical workstreams, mentor junior engineers, and promote software engineering best practices in consulting environments.

Build analytical frameworks, measurement methodologies, and decision support to drive enterprise decisions across marketing, loyalty, pricing, menu, media, and operations. Mine and integrate customer, transaction, digital, and operational data; develop segmentation, predictive models, and dashboards; lead test-and-learn, incrementality, and forecasting efforts; translate analysis into clear business recommendations and scalable decisioning tools.

Build, optimize, and maintain data pipelines, ETL processes, data architectures, AI solutions, and analytics platforms. Develop cloud-based solutions using Azure Functions and containerization, implement CI/CD workflows, integrate databases and APIs, and ensure data quality, monitoring, reliability, and documentation. Troubleshoot production issues, collaborate with cross-functional teams, and apply modern data engineering, AI, cloud, and DevOps practices.

Leads enterprise data strategy, governance, quality, architecture, and data product initiatives across scientific, technology, and operational functions. Develops data roadmaps, operating models, stewardship practices, and scalable data products while aligning senior stakeholders and cross-functional teams. Oversees scientific data programs from concept through adoption, drives data usability and interoperability, and mentors teams focused on governance, analytics, and data-enabled transformation.

The Staff Data Reliability Engineer ensures data integrity, quality, freshness, and availability across enterprise pipelines. Responsibilities include defining data SLOs, implementing observability, building resilient Informatica and Python/PySpark pipelines, automating validation and recovery, eliminating operational toil, leading data incidents and postmortems, creating runbooks, and applying AIOps, machine learning, and DataOps practices to detect anomalies and self-heal data workflows.

Architect, build, and evolve AWS-based data platforms and pipelines handling large, complex datasets. Optimize data solutions for scalability, reliability, performance, and cost efficiency. Collaborate with engineering and product teams, make architectural decisions, establish best practices, provide technical mentorship, and support modernization toward intelligent data architectures. The role involves Python, SQL, Databricks, Kubernetes, automation, DevOps, and agentic AI technologies.

Lead data quality and analytics for VA EHRM deployments by building and validating pipelines, applying SQL/R/Python and NLP/ML, defining governance rules in Purview/Collibra, certifying dashboards (Tableau/Power BI), and delivering actionable visualizations and insights to executive stakeholders.

Perform quantitative analyses (optimization, simulation) to support insurance and pension clients on asset allocation, ALM, portfolio construction, hedging, and analytics. Develop and improve production engines, custom analytical tools, and automated solutions. Interface with client service, portfolio management, and risk teams and vendor systems to deliver, explain, and operationalize bespoke client solutions.

Design, build, and maintain secure, scalable data pipelines and platforms (including Palantir Foundry/Gotham) for mission-driven clients in classified or restricted environments. Guide cross-functional teams, communicate architecture decisions, implement secure analytics ecosystems, and support AI/ML integration and DevSecOps practices under constrained mission conditions.

Design, develop, deploy, operationalize, and maintain enterprise-scale data applications, pipelines, and platforms. Process structured and unstructured data, build scalable ETL and ELT workflows, convert data into usable formats, and support monitoring and operation of data platforms. Collaborate with analysts, developers, engineers, and data consumers in an agile environment to deliver mission-driven solutions.

Architects and scales cloud-native analytics platforms using Databricks and Snowflake. Builds automated pipelines, data models, decision engines, and AI enablement layers that integrate POS, syndicated, and internal data. Develops campaign measurement algorithms, productionizes analytical models, optimizes data warehouse ecosystems, and mentors data engineers. Translates business requirements into scalable SQL and Python solutions while communicating technical capabilities to business stakeholders.

Leads specialty exposure management for non-natural catastrophe and liability portfolios across insurance lines. Oversees analysts performing pre- and post-bind modeling, portfolio production, reporting, regulatory submissions, data quality, and model validation. Partners with underwriting teams to optimize risk-adjusted capacity deployment, presents portfolio insights to senior stakeholders, manages outsourcing relationships, and advances exposure management tools, workflows, AI, automation, and analytics capabilities. Requires extensive insurance or reinsurance catastrophe modeling experience and strong leadership, risk governance, SQL, and analytical skills.

Designs and advances the firm’s enterprise data platform, including data warehouses, lakes, semantic models, pipelines, ETL/ELT processes, dashboards, and analytics solutions. Applies statistical analysis, forecasting, predictive modeling, machine learning, and AI-ready data architecture to improve business decisions and operational efficiency. Provides technical leadership in data strategy, governance, quality, architecture, automation, and emerging analytics technologies while partnering with business and technology stakeholders.

Designs and advances the firm's enterprise data and analytics capabilities, including data warehouses, lakes, semantic models, pipelines, dashboards, predictive models, machine learning solutions, and AI-ready data assets. The role ensures data quality, governance, scalability, and performance while partnering with business and technology leaders. Responsibilities also include translating business requirements into analytics solutions, evaluating emerging technologies, supporting AI applications, and providing technical leadership for data strategy and engineering best practices.

Design and advance the firm's enterprise data and analytics capabilities. Build data platforms, warehouses, lakes, semantic models, and scalable ETL/ELT pipelines. Develop Power BI and Microsoft Fabric dashboards, KPIs, and reporting solutions. Apply statistical analysis, predictive modeling, forecasting, and machine learning to business problems. Create governed, AI-ready data assets and support intelligent automation, generative AI, and retrieval pipelines. Provide technical leadership in data architecture, governance, analytics strategy, and engineering best practices.