Top Hybrid Data Science Jobs

Partner with business stakeholders to define analytics problems, prepare and join large datasets, build and automate models, interpret results, and present recommendations. Manage multiple projects, provide peer review and coaching, document procedures, and support process improvement across cross-functional teams.

Mid-level product analytics role partnering with product and engineering to analyze large datasets, design experiments, build statistical and ML models, measure success metrics, optimize user experience with GenAI-driven personalization, and deliver actionable insights and scalable analytics solutions to improve product outcomes.

Build and maintain financial data models and scalable reporting systems powering ARR, revenue, billings, and other metrics. Partner with Finance, Accounting, Investor Relations, Engineering, and Data teams to ensure data accuracy across close processes, forecast and analyze performance, and operationalize key financial metrics to support executive decision-making and public company reporting.

Lead data science work on the Matching team: translate product goals into analytics and ML problems, design and analyze A/B tests, mine data for product insights, and collaborate to develop and test production models that improve user matching.

The Data Scientist designs and develops statistical and machine learning models, collaborating across business functions to improve operations through data analytics and AI solutions.

As a Data Scientist at GLS, you will design data science solutions, develop predictive models, gather and clean data, and present insights to stakeholders while collaborating with business partners and IT.

Use statistical modeling and analytical frameworks to shape data acquisition, validation, and ML training strategies for AD/ADAS. Develop metrics to characterize data distribution and diversity, perform ambiguous problem analysis to inform decisions, communicate findings to stakeholders, embed with engineering teams for domain-specific recommendations, and mentor data science engineers to drive best practices.

Hands-on data scientist owning the full lifecycle of risk models for credit, accident, and fraud. Build, calibrate, deploy, and monitor predictive models and risk scores; manage model orchestration and vendor integrations; detect drift and recommend mitigations. Collaborate with Risk, Finance, Product, and Engineering to set thresholds and translate model outputs into actionable decisions.

The Data Scientist will analyze data to enhance business performance, engage customers, and optimize product strategies through experimentation and metrics establishment.

Support federal law enforcement by collecting, integrating, cleaning, analyzing, modeling, and explaining large datasets. Build ML/AI and graph-based models, produce visualizations and briefings for non-technical audiences, contribute to R&D and shared capabilities, and work on-site in Washington, DC. Must be a U.S. citizen able to obtain security clearance, with practical Python and SQL experience and at least three years of relevant data science work.