Top Hybrid Data Science Jobs in Boston, MA

The Manager of Data Science will lead a team focused on machine learning model development, team management, and cross-functional collaboration to advance product goals.

Leads enterprise data science strategy, governance, automation, and analytics enablement across therapeutic areas. Oversees pipelines, data architectures, dashboards, AI/ML adoption, data standards, regulatory compliance, and methodological consistency. Advises executives, partners with clinical and technology teams, manages analytics personnel, drives organizational change, and advances scalable data capabilities. Requires extensive healthcare or life sciences analytics experience, people leadership, and expertise in Python, R, SQL, visualization platforms, clinical data standards, and regulated environments.

Develop and refine machine learning and LLM-powered models for clinical use cases, from prototyping through production. Conduct rigorous evaluation and error analysis, identify failure modes, implement targeted improvements, and ship frequent model updates. Apply prompting, structured outputs, tool use, and evaluation frameworks while contributing to performance metrics, reporting, and dashboards. Collaborate closely with the ML team in a fast-paced, high-ownership environment.

Conduct econometric, causal inference, cost-benefit, and financial analyses evaluating policies and economic mobility programs. Acquire and manage large datasets, develop impact and investment models, create calculators, dashboards, and visualizations, and translate technical findings into policy insights. The role also produces technical and public-facing reports, supports data science education, mentors junior researchers, and presents findings to policymakers and other stakeholders.

Analyze autonomous vehicle, fleet, and business data to develop metrics, evaluate model and simulation performance, identify behavioral patterns, and support data-driven decisions. Projects may include drift monitoring, automated retraining, predictive modeling, similarity search, and statistical testing. The intern will use Python and SQL, apply machine learning and analytical methods, collaborate with simulation and autonomy teams, and communicate findings to varied audiences.

Leads organization-wide data science strategy, architecture, and AI/ML standards across multiple teams. Drives multi-year initiatives, platform and tooling decisions, scalable production systems, and measurable improvements. Provides technical leadership in generative AI, agentic systems, LLMs, deep learning, and advanced modeling. Influences executives and cross-functional roadmaps, mentors senior contributors, supports hiring, and promotes responsible AI, data governance, and innovation.

Leads end-to-end advanced analytics projects supporting regulatory operations and cross-market surveillance. Defines quantitative approaches, develops predictive pipelines, drives data and infrastructure availability, and delivers analytics capabilities across teams. Engages stakeholders, gathers use cases, provides research and insights, mentors analysts, and advances enterprise analytics strategy. Requires strong programming, machine learning, data mining, and analytical skills in a hybrid environment with occasional travel.

Curate and annotate investment datasets from infrastructure and private credit virtual data rooms, identify financial, legal, technical, and operational deal terms, and draft investment memos for AI model training and evaluation. Define annotation standards, manage labeling quality control, collaborate with AI/ML, product, and investment teams, and contribute to prompt engineering and AI-generated diligence tools.

Leads rigorous evaluation methodologies for autonomous vehicle performance using statistical analysis, machine learning, simulation, and on-road data. Develops metrics, detects drift and anomalies, identifies edge cases, correlates simulation with fleet data, and strengthens launch-readiness and safety decisions. Partners with engineering, safety, and operations teams, communicates findings to leadership, establishes developer self-service tools, and mentors colleagues.

Leads end-to-end data science projects for automated underwriting and related insurance analytics. Develops statistical and machine-learning models, evaluates performance, analyzes complex datasets, and implements scalable production solutions. Partners with underwriting, product, business, engineering, and technology teams; monitors models; documents governance and validation processes; evaluates external data and third-party solutions; provides technical direction; reviews work; and mentors other data scientists.