Top Hybrid Data Science Jobs in Boston, MA

Build predictive models for churn, expansion, and look-alikes; run marketing mix and incrementality analyses; create leading indicators and cohort reporting; partner with Sales, Marketing, and RevOps to translate analytics into actionable recommendations and inform data infrastructure needs.

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

Leads the strategy for scaling data science analyses used in pharmaceutical asset acquisition and diligence. Directs cross-functional Data Science and Engineering teams, determines which one-off analyses should become landscape analyses or durable tools, sets roadmaps, evaluates statistical and technical validity, identifies AI opportunities, and connects scientific findings to business impact, asset valuation, and sourcing decisions.

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.

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.

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.

Develop data science solutions for Trust and Safety, including fraud and abuse detection. Responsibilities include gathering and preparing data, exploratory analysis, feature engineering, statistical analysis, machine learning, model evaluation, and communicating insights. The role partners with Product, Engineering, Operations, and Business stakeholders to deliver models, dashboards, recommendations, and operational improvements, with emphasis on rare-event detection and imbalanced datasets.

Leads product vision, roadmap, prioritization, and delivery for data science enablement capabilities supporting enterprise AI and machine learning solutions. Serves as Product Owner, translating stakeholder and data scientist needs into backlogs and technical product decisions. Partners with engineering, architecture, and data science teams on model development, deployment, monitoring, MLOps, DevOps, technical debt reduction, and platform adoption. Communicates priorities, outcomes, and recommendations to stakeholders and leadership.

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.

Leads the Partnerships Customer Management Data Science team, mentoring specialists and owning the strategic roadmap for predictive modeling across credit risk, account usage, and portfolio financials. Oversees end-to-end machine learning model development, validation, and production implementation. Partners with analysts, engineers, and product managers while applying Python, AWS, Spark, H2O, and other technologies to analyze large-scale data and deliver customer-focused business outcomes.