Top Hybrid Data Science Jobs in Boston, MA

Supports data science teams by cleaning and processing structured and unstructured data, engineering features, executing and monitoring machine learning pipelines, generating insights, investigating trends and risks, and collaborating with senior data scientists and business analysts. The role requires a current MIT quantitative-field bachelor's student working 10–20 hours weekly in the continental United States.

An 11-week data science internship involving research, data gathering and analysis, exploratory and regression analysis, model development and evaluation, and business recommendations. Interns work independently and collaboratively while developing skills in GenAI, machine learning, statistical modeling, and business analytics.

Develop predictive and prescriptive models using structured and unstructured data, with a focus on NLP and GenAI. Responsibilities include exploring datasets, building visualizations, generating business insights, automating data workflows, developing interactive applications, and collaborating with stakeholders across marketing, sales, finance, and supply chain.

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.

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 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.

Manage delivery of a portfolio of AI and data science projects from planning through implementation. Coordinate technical and non-technical teams, dependencies, requirements, budgets, timelines, and stakeholder expectations. Lead Agile ceremonies, drive accountability, translate AI capabilities into actionable requirements, and proactively identify and mitigate risks across complex, concurrent initiatives.

Leads data scientists and machine learning engineers developing personalized customer experiences at scale. Sets team strategy, roadmaps, and priorities; partners with Product, Engineering, Marketing, Revenue, and Data Science leaders; guides machine learning systems through development, deployment, monitoring, and optimization; advances MLOps, software engineering, experimentation, and production reliability; and manages capacity, resources, technical standards, and team development.

Lead and grow a Data Science team for Provider Products, set analytical direction, own experimentation and measurement, review methods, partner with product and engineering, hire and develop data scientists, standardize analytics and integrate generative AI to improve workflows and outputs.

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.