Top Hybrid Data Science Jobs

This role involves onboarding clients, solving scientific problems, guiding data analysis on the Tempus platform, and creating documentation. Requires expertise in biomedical datasets and programming.

Lead product-focused data science and analytics work: define success metrics, design and analyze A/B tests, investigate product and customer performance, build anomaly detection frameworks, and translate insights into product and roadmap recommendations while partnering with cross-functional teams.

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Lead development of scalable analytics and evaluation systems for LLM and AI-powered products. Partner with execs, product, and engineering to define metrics, apply statistical and ML methods to measure and forecast product and system performance, automate analyses and self-serve insights, and mentor junior data scientists while owning evaluation pipelines and AI-native integrations.

Leads a data science team focused on customer intelligence and personalization across digital products, apps, newsletters, and marketing channels. Develops lifetime value, segmentation, retention, recommendation, attribution, forecasting, and engagement models. Owns product funnel analytics and experimentation, including A/B testing and causal inference. Partners with Product, Marketing, Editorial, Data Operations, and Engineering to translate customer behavior into product improvements, measurable growth, and scalable data capabilities.

Lead data strategy for large 2D and 3D media datasets used in production machine learning. Coordinate with ML, annotation engineering, and technical program management teams; translate project goals into actionable data specifications; oversee dataset curation, filtering, and processing; and ensure data integrity, representativeness, and quality through analysis and inspection.

Design, build, validate, deploy, and monitor ML models (credit risk, fraud, transaction timing). Create training datasets, run A/B tests, analyze customer behavior, and collaborate with Engineering, Product, and Analytics to deliver end-to-end data science solutions.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

Lead Sprout's centralized Decision Analytics function to transform reporting into predictive, insight-driven decision-making. Set vision, operating model, and standards; manage managers, analysts, and data scientists across domain pods; partner with senior stakeholders; champion AI and ML integration; drive change management and build a high-impact analytics culture.

The Applied Scientist role involves developing quantitative models for decision-making under uncertainty, focusing on pricing, demand modeling, and risk management. Responsibilities include building and optimizing models, collaborating with engineering and product teams, and translating complex problems into actionable solutions.