Top Hybrid Data Science Jobs in Boston, MA

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

Use advanced analytics, statistical methods, and machine learning to transform large datasets into actionable insights. Build data solutions and pipelines, perform exploratory analysis, create visualizations, validate models (including generative AI and NLP), collaborate with clients, mentor juniors, and maintain data quality to inform strategic decisions.

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

As a Data Science Manager, you will lead a team in developing personalized experiences through machine learning, aligning efforts with product and engineering to drive impact and innovation.

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.

The Manager of Data Science will lead a team focused on machine learning model development, team management, and cross-functional collaboration to advance product goals.

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 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.

As a Data Scientist, you will design and operationalize advanced analytics, develop scalable models, and collaborate with teams to enhance product capabilities in healthcare.

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.