Top Hybrid Data Analysis Jobs

Analyze, clean, validate, and reconcile data to produce dashboards, reports, metrics, SQL queries, analytical datasets, and business insights. Partner with stakeholders to define requirements, investigate trends and anomalies, validate data models and workflows, improve data quality, and establish scalable analytics standards and documentation.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, experimentation, campaign reporting, retention analysis, churn modeling, segmentation, and LTV analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and deliver actionable recommendations. Requires strong SQL and Python skills, causal measurement expertise, marketing platform experience, and the ability to communicate insights to senior stakeholders.

Analyzes connected-vehicle customer journeys, system performance, operational workflows, and production incidents. Builds dashboards and reports, writes SQL queries, investigates root causes, detects trends and anomalies, and translates findings into corrective actions. Partners with engineering and contact-center teams to resolve customer-impacting issues, improve connected-service reliability, define metrics, validate changes, and establish scalable operational practices.

Develop and optimize advanced SQL transformations, stored procedures, ETL workflows, and data-quality processes for large Medicaid claims and health-related social needs datasets. Build reproducible, documented data pipelines; calculate healthcare quality, equity, and disparity metrics; conduct code reviews; and collaborate with stakeholders to turn complex data into visualizations and policy insights.

Conduct quantitative research for algorithmic trading and market-making strategies. Design, test, deploy, and monitor models supporting pricing, risk, and execution. Build research and backtesting tools, analyze large datasets and model outputs, and collaborate with traders, developers, and senior researchers to translate hypotheses into production trading decisions. Continuously improve strategies based on market dynamics, data drift, and performance results.

Leads product analytics and partners with product and executive leadership to guide strategy, roadmap prioritization, experimentation, and optimization. Translates customer and business questions into measurable outcomes, analytical roadmaps, segmentation, funnel and cohort analyses, and evidence-based recommendations. Owns product measurement frameworks, dashboards, data quality, instrumentation, and governance while communicating insights, trade-offs, and actions to senior stakeholders.

Develop data models, analyses, dashboards, and automated reporting for a collectibles marketplace. Partner with engineering, product, and data platform teams to define business metrics, identify customer experience and growth opportunities, inform strategy, and lead initiatives that improve business performance.

Lead consumer lending analytics and strategy across credit card, HELOC, and Auto/RV products. Evaluate portfolio performance, member behavior, pricing, targeting, risk, profitability, and growth opportunities. Develop strategic recommendations, forecasts, business cases, and analytical frameworks using SQL and large datasets. Partner with finance, credit risk, marketing, operations, and technology leaders while managing, coaching, and developing an analytics team.

The Senior Data Scientist will lead analytics projects, define success metrics, run experiments, and present data-driven insights to influence product and business strategies.

Develop causal economic analyses using Mastercard and public datasets. Translate business questions into identification strategies, build econometric and causal machine-learning models, lead projects from data ingestion through validation, and communicate findings to clients and stakeholders. Guide best practices, prototype scalable analytics, conduct research, and apply experimentation methods across business contexts.