Top Hybrid Data Science Jobs in Boston, MA

Leads healthcare payer data science initiatives involving claims, membership, and operational data. Develops statistical models, predictive algorithms, and machine learning solutions for risk adjustment, utilization, and population health. Advises clients on payer modernization and operating model improvements, translates findings into visualizations and recommendations, coordinates multidisciplinary workstreams, validates data and models, and coaches analytical teams through complex client-facing projects.

Leads AI and machine learning solution design, deployment, evaluation, and data engineering across client engagements. Establishes portfolio-wide standards for AI quality, robustness, bias, explainability, monitoring, and technical evidence. Advises clients on AI strategy, coaches multidisciplinary teams, sponsors reusable evaluation capabilities, guides business development, and builds executive client relationships while directing multiple workstreams.

Leads payer healthcare data science engagements, developing statistical and machine learning models from claims, member, provider, and utilization data. Directs analytical strategy, exploratory analysis, visualization, feature engineering, validation, and technical problem solving. Advises executive clients on payer modernization, builds business relationships, identifies growth opportunities, mentors teams, and contributes thought leadership in AI, advanced analytics, and healthcare transformation.

Lead and develop a team of data scientists and AI/ML engineers delivering scalable, production-grade AI solutions. Drive reusable AI products, platforms, APIs, frameworks, and enterprise architecture. Establish engineering practices including testing, CI/CD, observability, documentation, and MLOps/AIOps. Partner with product, engineering, architecture, and business stakeholders to accelerate AI adoption and transformation across global insurance operations.

Leads data science partnership for insurance pricing teams, identifying AI opportunities to accelerate product rollout. Builds evaluations for AI and agentic systems, including metrics, ground truth, statistical tests, experiments, and production testing. Measures value and risk, prototypes evaluation tools and AI applications, and translates pricing and business needs into technical requirements. Requires insurance pricing model experience, advanced statistics, Python coding, modern LLM expertise, and strong cross-functional communication.

Lead end-to-end data science initiatives spanning ideation, modeling, experimentation, deployment, and continuous improvement. Build predictive, statistical, causal inference, forecasting, and machine learning models, plus AI copilots and assistants for marketing workflows. Partner with marketing stakeholders to identify high-impact opportunities, communicate insights, and operationalize solutions. The role requires strong Python skills, advanced statistical and machine learning knowledge, rapid prototyping ability, and experience solving complex business problems at scale.

Leads advanced analytics, machine learning, and AI initiatives for Finance, developing predictive models, forecasting solutions, optimization, automation, anomaly detection, and conversational analytics. Partners with Finance, Data, Technology, and Engineering teams to translate business challenges into scalable solutions, establish governance and testing frameworks, evaluate model performance and bias, and drive adoption. Provides technical leadership and mentorship while supporting Finance transformation and continuous improvement.

Lead measurement and data science strategy for Klaviyo’s AI agents, Composer and Customer Agent. Develop frameworks for adoption, quality, engagement, customer value, cost, and revenue impact. Apply experimentation, causal inference, statistical modeling, conversation analysis, human evaluation, and LLM-based evaluation. Partner with Product, Engineering, Finance, and executives, build governed metrics and scalable analytics, influence product and pricing decisions, and mentor data scientists.

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.

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.

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.