Top Data Science Jobs in Boston, MA

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Develop predictive pricing and underwriting models for property and specialty insurance products. Responsibilities include manipulating large datasets, testing hypotheses, evaluating statistical significance, creating visualizations, supporting pricing program deployments, adjusting models for state regulations, preparing regulatory documentation, and responding to Department of Insurance objections. The role collaborates with business stakeholders and data science teams to deliver data-driven solutions and explain technical findings to nontechnical audiences.

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.

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.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Own analytics for Bevi digital experiences and websites, measure incremental impact of new flavors using experiments and quasi-experimental methods, build a flavor recommendation engine, create dashboards, and partner with Product, Design, Engineering, and Data Engineering to improve tracking, data quality, and product decisions.

Develops reproducible Python-based data analyses and bioinformatics pipelines for life sciences experiments. Translates wet-lab workflows and scientific questions into validated analytical requirements, integrates workflows with data lake and AI systems, and builds reusable analysis modules. Collaborates with scientists, automation specialists, software engineers, and ML engineers while identifying data, LIMS, and workflow gaps and documenting methods, assumptions, and results.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independent data work, strong analytical and communication skills, first-principles problem solving, and ownership of ambiguous initiatives through measurable business impact.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, first-principles thinking, stakeholder influence, and end-to-end ownership of ambiguous problems.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring data, solving ambiguous problems, communicating findings persuasively, and driving measurable business outcomes.

Own end-to-end data science and analytics for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, extract and structure data, conduct analyses and experiments, build reports, communicate insights, and drive data-informed decisions with cross-functional stakeholders. The role requires strong analytical thinking, independent problem ownership, and persuasive communication.

Own the analytical function for an embedded business unit across growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations to leadership; and drive decisions and outcomes end to end. The role requires independent data work, first-principles problem solving, strong communication, and ownership of ambiguous, high-impact initiatives.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

Uses product analytics, experimentation, forecasting, and behavioral analysis to improve Xbox player experiences and business outcomes. Partners with Product, Engineering, and business teams to analyze acquisition, engagement, retention, monetization, and lifecycle behavior. Defines metrics, evaluates A/B tests, identifies growth opportunities, builds reusable analytical assets, and improves telemetry and measurement infrastructure.

Lead advanced research and predictive modeling on residential housing and energy performance data. Apply causal inference, anomaly detection, regression, and model validation to improve Pearl’s SCORE and energy models. Develop new performance metrics, evaluate climate-risk data, collaborate with external research partners, and communicate findings through white papers, publications, and clear stakeholder explanations.

Develop and deploy production-grade AI, machine learning, and generative AI solutions for insurance business challenges. Responsibilities include building LLM applications, prompt engineering, RAG workflows, data pipelines, experiments, proof-of-concepts, and scalable AI systems. The role involves monitoring model performance, generating business insights, collaborating with stakeholders, and communicating technical findings to diverse audiences.

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

Provides healthcare analytics supporting quality, affordability, access, and consumer experience initiatives. Maps clinical journeys, analyzes quality variation, mines complex datasets, develops dashboards, tests hypotheses, and delivers strategic recommendations. Collaborates with cross-functional stakeholders to define business questions, analytical requirements, and reporting specifications, with a focus on Commercial health plan quality and Medicare Star ratings improvement.