Top Hybrid Data Science Jobs in Boston, MA

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

As a Lead Data Science Analyst, you will develop predictive models, conduct statistical analyses, and create AI-driven solutions to enhance GTM strategy and customer outcomes.

The Senior Data Scientist will implement and test ML models for international products, collaborating with cross-functional teams to drive insights and deploy solutions.

The Senior Data Scientist will design and implement AI-enabled products using clinical trial data, mentor team members, and collaborate with stakeholders.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Lead data science projects to advance drug development, implementing models for patient selection and clinical trial optimization, and collaborating with cross-functional teams.

The Data Scientist II will lead RWE studies, derive insights from real-world clinical data, and contribute to methodologies using advanced statistical techniques and AI tools, collaborating closely with pharmaceutical partners.

The Senior Data Scientist will engage with business partners to solve problems using data science, lead model development, and mentor others, focusing on supply chain and causal modeling.

Design and implement quantitative behavior metrics for autonomous vehicles, build and maintain data pipelines and dashboards, analyze large-scale vehicle logs and simulation data to identify regressions, set metric thresholds to support geofence expansion, and communicate findings to technical and non-technical stakeholders.

Own end-to-end data science and ML features for logistics: problem framing, modeling, production deployment, monitoring, and cross-functional delivery. Focus areas include theft detection, routing intelligence, anomaly detection, and customer-facing alerting using geospatial and IoT sensor data.