Top Data & Analytics Jobs

Build predictive models and customer economics analyses that forecast behavior, optimize marketing investments, and support revenue growth. Develop automated tools, dashboards, and repeatable frameworks to communicate customer performance and retention trends. Partner with Marketing, Finance, Data Science, and Data Engineering to translate complex model outputs into actionable business strategies using technologies including Databricks, Airflow, Bitbucket, SQL, and Snowflake.

Build predictive models forecasting customer behavior, analyze retention and gameplay trends, and create automated tools, analyses, and dashboards. Translate model outputs into actionable recommendations that guide marketing investments, promotional strategies, customer valuation, and retention. Partner with Marketing, Finance, Data Science, and Data Engineering teams while using Databricks, Airflow, Bitbucket, SQL, Snowflake, and dashboarding tools to support revenue growth and customer insights.

Leads enterprise competitive intelligence capabilities, including data collection, matching, scraping, governance, quality standards, analytical methodologies, and AI-enabled workflows. Partners with Product, Engineering, Data Science, Merchandising, Pricing, Digital, Marketplace, and Strategy teams to translate business needs into scalable solutions. Develops metrics, models, validation processes, and insights that support business decisions, while potentially managing and developing a small analytics team.

Leads data ownership and governance for data products, defining metadata, quality, controls, integration, and risk mitigation. Partners with business, analytics, technology, and engineering teams to deliver curated datasets and production-ready solutions. Uses advanced SQL for validation and root-cause analysis, manages workstreams and staff, and supports analytics and machine learning use cases. Applies AI and LLM tooling to automate governance activities and maintains data integrity across regulated environments.

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Lead statistical measurement and client intelligence initiatives using healthcare, prescription, and claims data. Develop performance benchmarks, evaluate program impact, apply statistical and machine learning methods, advise data scientists, support survey-based measurement, manage analytics vendors, mentor analysts, and present findings to marketing teams and clients. The role requires advanced SQL and Python, healthcare data expertise, vendor management, and extensive quantitative experience.

Own and advance the enterprise commercial data strategy across Sales, Insights & Analytics, Customer Experience, and related functions. Develop the data roadmap, evaluate and govern syndicated, retail, shopper, brand, and e-commerce data assets, and manage vendor contracts and performance. Lead data quality, regional support, and cross-functional alignment while piloting AI and machine learning capabilities for commercial data workflows.

Monitor fraud trends in retail deposit acquisition, analyze large datasets, identify defensive gaps, and develop strategies balancing fraud losses, operational costs, and customer impact. Partner with cross-functional and technical product teams to deliver fraud prevention capabilities, present actionable insights to senior leadership, and support business growth while protecting customers.

Develops automation, data pipelines, databases, analytics, machine learning models, reporting tools, and web applications for package technology quality assurance. Analyzes manufacturing, product, and reliability data to support risk assessment and root-cause investigations. Collaborates with engineering teams, communicates findings, trains users, maintains software and reports, and shares analytics best practices across global teams.

Design, build, deploy, and support scalable cloud data platforms, pipelines, and applications for risk management. Lead technical direction across data engineering, analytics, and machine learning teams; establish reusable engineering patterns; implement security and privacy controls; and collaborate with product managers and software engineers. The role includes mentoring, stakeholder communication, distributed data processing, data modeling, warehousing, streaming, and both relational and NoSQL database solutions.

Leads large-scale data engineering initiatives for Risk Tech, designing and delivering scalable cloud data platforms, pipelines, and applications. Responsibilities include architecture, data modeling, distributed processing, cloud warehousing, AI-enabled risk solutions, platform evaluation, operational resilience, and technical mentoring. The role collaborates with product managers, software engineers, analysts, and data scientists while independently driving architectural decisions and communicating technical outcomes to stakeholders.

Designs and leads scalable batch and real-time data pipelines supporting AI/ML applications, LLMs, RAG workflows, predictive models, and executive data products. Responsibilities include data architecture, feature stores, governance, security, lineage, performance and cost optimization, and cross-functional technical leadership. The role partners with data scientists, program managers, executives, and engineering teams to deliver reliable enterprise data solutions.

Analyze customer behavior across digital and phone journeys, develop experiments and OKRs, and translate data into actionable recommendations. Partner with product, technology, design, modeling, and analytics teams to improve customer experiences and operational efficiency. Present insights to senior stakeholders, support predictive analytics and machine learning initiatives, and maintain data governance through collaboration with data owners.

Leads enterprise healthcare data engineering strategy, cloud data platform modernization, warehouse and lakehouse architecture, ETL/ELT pipelines, streaming, interoperability, governance, data quality, and master data management. Manages data engineering, analytics, and platform teams while supporting AI/ML initiatives. Requires extensive healthcare data experience, leadership of large programs, cloud and data engineering expertise, knowledge of claims and clinical systems, and familiarity with HL7, FHIR, EHR platforms, and healthcare compliance.

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Design secure, resilient, and operationally efficient data systems, including streaming and AI-enabled solutions. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical concepts to stakeholders.

Analyze player behavior, product performance, revenue, retention, and engagement for Zynga Poker. Design experiments, dashboards, ETL pipelines, telemetry, fraud models, and business intelligence solutions. Partner with product, game design, economy, and consumer insights teams to guide decisions and improve player experiences. Contribute to AI-enabled analytics workflows, game integrity initiatives, and analytics best practices.

Build data pipelines and relational data models using Python and SQL; clean operational data, support scenario analysis, and create dashboards for PMO leadership. Explore LLMs and machine learning for text analysis and data-quality automation. Gain production experience with testing, documentation, maintainable code, and data-driven decision support in a trading environment.

Analyze user growth, engagement, and revenue data to identify trends, develop actionable insights, and guide product strategy. Lead forecasting for user growth and engagement, support performance management, create dashboards and reporting, and improve analytics foundations with data science, product, and engineering partners. Present findings to senior leadership and contribute to product initiatives, metric development, and business operations.

Analyze and optimize the end-to-end sales funnel by developing KPIs, dashboards, diagnostic analyses, and operational measurement frameworks. Partner with Sales, Operations, Data Engineering, and Data Science teams to improve conversion, revenue, capacity planning, and pipeline prioritization. Lead AI-native automation initiatives, including LLM analytics assistants, automated reporting, prompt engineering, and machine learning model validation.

Builds and maintains automated dashboards, reporting, KPIs, and analytical tools for transplant logistics. Mines and transforms large datasets using SQL and Python, applies statistical analysis, and delivers actionable insights to Product, Finance, Logistics, Clinical, and Technology teams. Supports logistics network design and evaluates big data technologies while translating business needs into scalable data solutions.

Leads enterprise sanctions-screening analytics strategy, developing KPI frameworks, governance standards, dashboards, root-cause analyses, and predictive insights. Identifies false-positive drivers, workflow bottlenecks, and automation opportunities while recommending tuning and process improvements. Partners with technology, operations, compliance, and governance stakeholders to build business cases, implement analytics-enabled automation, monitor performance, and deliver executive-ready reporting.

Develops fraud rules, models, dashboards, and strategies to reduce fraud losses. Analyzes emerging fraud trends, monitors KPIs, conducts ad hoc analysis, supports production deployments, and communicates findings to technical and business stakeholders. The Thursday-through-Monday schedule requires weekend availability, with a hybrid arrangement based in Chicago.

Conduct quantitative research for systematic FX options and volatility strategies. Develop forecasting models, trading signals, market-making approaches, and systematic hedging strategies. Own the full research lifecycle, including data analysis, feature engineering, backtesting, validation, portfolio construction, performance attribution, stress testing, and live deployment. Collaborate closely with traders and engineers, build analytical tools, monitor production performance, and communicate research findings, assumptions, and risks to senior stakeholders.

Build and modernize data systems for scientific and engineering data. Responsibilities include designing automated pipelines, developing schemas and metadata practices, implementing data quality and lineage, building APIs and dashboards, applying DataOps and software engineering practices, evaluating data platforms, and collaborating with scientists, engineers, governance teams, and stakeholders. The higher level independently solves complex data management problems, sets technical direction, mentors others, and advises on data strategy and architecture.

Lead operations research and data analytics supporting Department of War acquisition, sustainment, workforce, and decision-analysis initiatives. Develop analytical products, predictive models, dashboards, business intelligence solutions, reports, data frameworks, and quality documentation. Collaborate with government leaders and cross-functional stakeholders to analyze disparate datasets, streamline reporting, and provide data-driven findings and recommendations.