Top Data Science Jobs in Boston, MA

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Develops data integration workflows, dashboards, statistical and predictive models for biotherapeutics process development. Analyzes complex scientific and process data, identifies insights and anomalies, troubleshoots data and analysis issues, and communicates findings to technical teams. The role also supports project deliverables, external collaborations, process improvements, and training junior staff on data tools, analysis methods, and visualization practices.

Lead real-world evidence studies using complex clinical and molecular data, observational study design, survival analysis, causal inference, and machine learning. Collaborate with pharmaceutical partners, derive clinical endpoints, interpret results, develop reusable analytical code, and apply LLM-based tools to accelerate workflows. Communicate findings to technical and non-technical stakeholders through reports, presentations, manuscripts, and conference materials while partnering cross-functionally to improve data quality and analytical practices.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Build and deploy machine learning models and scoring platforms for credit decisioning and valuation. Analyze large-scale financial and textual datasets using statistical modeling, cloud technologies, and open-source tools. Responsibilities include model design, training, validation, implementation, analytical tool development, alternative data analysis, and collaboration with software engineering and product teams to deliver scalable credit products.

The Data Scientist will analyze complex healthcare datasets, build Python-based reporting pipelines, clean and validate data, and create tables, visualizations, and PowerPoint presentations. They will identify cost and utilization drivers, answer account questions, improve client-facing analytics, and collaborate with underwriting, medical management, sales, finance, and other business partners. The role requires translating complex analyses into actionable insights while managing multiple projects and deadlines.

Develop forecasts, machine learning models, and optimization algorithms for renewable energy and battery storage assets. Analyze large energy-market datasets, build simulations, deploy production-grade data science workflows, create dashboards, and establish asset-optimization metrics. The role collaborates with engineering, operations, and energy markets teams to support real-time operations, market strategies, and business development.

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.

Leads data science initiatives for US Card acquisitions, developing and deploying machine learning models across their full lifecycle. Partners with data scientists, software engineers, product managers, and business stakeholders to solve complex problems, analyze large-scale numeric and textual datasets, validate models, and translate technical findings into business outcomes. The role also involves team leadership, talent development, model governance, monitoring, and ongoing model improvements.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Supports defense-focused applied R&D by developing machine learning and AI systems, data pipelines, simulations, analytical models, dashboards, and validation processes. Collaborates with academic, government, and industry partners; translates findings for leadership and sponsors; contributes to Navigator methodology, proposal development, project scoping, and special initiatives. The role requires strong statistical modeling, ETL, software engineering, visualization, communication, and the ability to obtain a security clearance.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Create reproducible data science and machine learning tasks with verified reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify implementation errors and data leakage, refine task designs, and document requirements and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical expertise in implementing and validating solutions.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Builds workforce analytics, visualizations, statistical analyses, time-series forecasts, and machine learning models using HR and clinical data. The role predicts staffing needs and employee attrition, analyzes pay and burnout relationships, supports pay equity and recruitment optimization, and translates HR strategies into measurable KPIs. Responsibilities include deploying models on cloud platforms, conducting experiments, protecting employee data, and communicating insights to technical and non-technical stakeholders.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.