Top Hybrid Data Science Jobs in Boston, MA

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Develops claim-level reserve modeling and predictive analytics for personal and commercial insurance lines. Responsibilities include mining large datasets, applying statistical and machine learning techniques, testing hypotheses, building predictive models, creating accessible visualizations, and translating findings into business decisions. The role also advances analytical methodologies, guides complex projects, advises stakeholders, customizes solutions, and leads cross-functional data science initiatives.

Leads AI and data science initiatives that accelerate insurance pricing program rollouts. Builds evaluations for AI and agentic systems, defining accuracy metrics, ground truth, statistical tests, edge cases, and production experiments. Measures business value and risk, prototypes evaluation tooling and AI applications, and partners with pricing, technology, delivery, and business teams. Requires insurance pricing model experience, advanced statistics, strong Python coding, modern LLM experience, and the ability to communicate complex findings to stakeholders.

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 data science for Capital One’s US Card business, developing and deploying machine learning models across marketing, acquisitions, underwriting, and fraud prevention. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using cloud and open-source technologies; translates complex insights into business outcomes; and develops talent while guiding technical innovation.

Leads enterprise data science and AI solution delivery, including ML, generative AI, and predictive model development, deployment, monitoring, governance, and integration into business workflows. Drives AI adoption, change management, value realization, and evaluation of emerging technologies while partnering with data, engineering, and platform teams. Builds and leads AI delivery teams and ensures compliance with security and governance standards.

Lead the development of deep learning and computer vision models for MRI, X-ray, CT, and PET medical imaging in clinical trials. Build image processing pipelines, analyze disease progression and treatment response, develop imaging biomarkers, integrate AI into trial workflows, support FDA-compliant endpoints, and present findings to clinical, regulatory, executive, and external stakeholders.

Leads applied machine learning and statistical research for health features using longitudinal wearable, physiological, behavioral, and multimodal data. Designs studies, models, evaluation protocols, and quality standards; partners with engineers to productionize methods; advises product, clinical, regulatory, and digital health teams; mentors scientists and engineers; and communicates evidence, limitations, risks, and trade-offs to technical and executive stakeholders.

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 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.