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 product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

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.

Analyze billing patterns, healthcare claims, and complex datasets to identify anomalies, audit data integrity, and support Trust and Safety initiatives. Build risk-monitoring pipelines and compliance dashboards using SQL, Python/R, and visualization tools. Translate technical findings into risk assessments for legal, compliance, and executive partners while developing safeguards to protect patients, regulatory standing, and platform operations.

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.

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.

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.

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

Develop and maintain predictive models and data pipelines to support player evaluation, acquisition, development, and performance optimization. Produce clear reports and visualizations, communicate findings to technical and non-technical stakeholders, integrate analytics with team systems, monitor data quality, and evaluate cutting-edge research to recommend improvements that enhance on-field performance.

Lead global performance analytics across demand, inventory, product, and channel views. Translate ambiguous business questions into scalable analytical solutions, develop forecasting and advanced models, improve data quality, and productionize reusable workflows. Partner cross-functionally with Technology and Planning leaders, align metrics across regions, and communicate executive-ready recommendations. Apply Python, SQL, AI/ML, generative AI, and emerging technologies to improve decision-making, growth, profitability, inventory productivity, and operational efficiency.

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.

Designs, deploys, and maintains AI, machine learning, and generative AI solutions supporting sales forecasting, pipeline analytics, lead scoring, customer segmentation, and seller productivity. Builds predictive models, AI copilots, assistants, and RAG solutions; analyzes CRM and operational data; develops dashboards and executive reporting; and establishes MLOps monitoring. Partners with sales, revenue operations, and IT leaders to identify use cases, deliver scalable solutions, and drive data-informed commercial decisions.

As a Senior Data Scientist, you will develop technologies for signals exploitation, perform data analysis, and implement algorithms while collaborating with project teams.

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.

Researches, develops, evaluates, and integrates AI and machine learning models for national security applications. Analyzes multimodal datasets, including text, imagery, geospatial, time series, and social media data, to identify patterns and generate intelligence insights. Collaborates with researchers, engineers, domain experts, customers, and end users; communicates findings, defines requirements, responds to feedback, and mentors teammates. Work primarily occurs onsite in Woburn, Massachusetts, or Arlington, Virginia.

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

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

Develop high-fidelity discrete-event and agent-based simulations for large-scale robotic warehouse systems. Build predictive and prescriptive models, digital twins, and optimization solutions for throughput, latency, scheduling, task allocation, and resource utilization. Apply machine learning and statistical methods to calibrate simulations, identify bottlenecks, and evaluate system changes. Partner cross-functionally, communicate recommendations to stakeholders, establish validation and reproducibility practices, and, at the Principal level, define strategy and mentor technical teams.

Analyze and integrate single-cell and spatial multi-omics data; develop scalable, reproducible pipelines, statistical models, AI-assisted research tools, open-source software, and biomedical data resources. Collaborate with experimental labs, provide computational biology guidance and training, create workshops and documentation, and support methodological development, follow-up experiments, and scientific data interpretation.