Top Hybrid Data Science Jobs

Build and deploy machine learning models and end-to-end pipelines for marketing targeting, customer outcomes, and loan origination. Responsibilities include feature engineering, experimentation, model evaluation, monitoring, documentation, governance, and translating analytical findings into business recommendations. The role collaborates with marketing, analytics, product, and cross-functional stakeholders to deliver explainable, compliant solutions.

Develop predictive models and data science solutions for automated credit-risk and lending decisions. Responsibilities include collecting, cleaning, visualizing, and analyzing large datasets; identifying patterns; presenting insights to stakeholders; translating models into business strategies; partnering with IT on implementation; and monitoring model performance.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Transforms hardware quality data into strategic insights through statistical analysis, correlation studies, predictive modeling, automated data pipelines, monitoring systems, and data visualizations. Connects quality metrics across the product lifecycle, identifies root causes and data gaps, and develops automation solutions. Partners with quality, engineering, operations, and executive teams to improve workflows and support strategic decisions through clear, persuasive data storytelling and presentations.

Lead growth data science for emerging healthcare products by defining measurement frameworks, analyzing funnels, designing experiments, building predictive models and segmentations, and creating reliable data foundations, dashboards, and self-service tools. Partner with Marketing, Product, Engineering, Design, and Data Science to optimize acquisition, engagement, conversion, launches, and product growth.

Lead the end-to-end development, deployment, monitoring, and optimization of machine learning models supporting healthcare operations. Translate business goals into measurable data science objectives, design impact experiments, maintain reliable production ML systems, monitor model health and drift, manage incident response, and guide technical strategy. Mentor data science and ML engineering teams while partnering with Product, Engineering, and leadership. The role also involves healthcare data, real-time systems, EHR integrations, and improving operational efficiency.

Lead end-to-end data science efforts across pricing, dispatch, carrier selection, ETA prediction, routing, and forecasting. Own discovery through production, build models and pipelines with Python/SQL/Snowflake, partner with engineering to serve models, establish evaluation/monitoring/A-B testing, and measure impact on cost, reliability, and delivery performance while working closely with product, operations, and customers.

As a Data Scientist, you will advance applied ML capabilities for healthcare, develop LLM-based chart abstraction techniques, and enhance data science best practices.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Lead and coach a team of four data scientists to build productionized analytics and models for B2B Sales and Customer Success. Set roadmap with GTM partners, deliver forecasting, pipeline and health scoring, champion AI-driven automation and self-serve analytics, and represent insights to senior commercial stakeholders.