Top Hybrid Data Science Jobs in Boston, MA

Lead AI and data science efforts that accelerate insurance pricing program rollouts. Partner with pricing, delivery, and technology teams to identify AI opportunities, build evaluations for AI and agentic systems, define metrics and statistical tests, measure value and risk, and develop evaluation tooling and AI applications. Apply insurance pricing expertise, modern LLM experience, experimental design, and strong stakeholder communication to move high-value solutions into production.

Leads health economics and outcomes research and real-world evidence projects for pharmaceutical, biotech, academic, and other partners. Designs and analyzes pharmacoepidemiologic studies, treatment patterns, healthcare utilization, causal inference, and survival analyses. Builds reusable analytical infrastructure, collaborates with scientists, engineers, and product teams, interprets results for stakeholders, supports publications and regulatory work, and ensures compliance with data governance and applicable regulations.

Leads healthcare payer data science and modernization initiatives, developing predictive models, statistical analyses, and scenario-based insights from claims, reimbursement, utilization, and member experience data. Directs multiple analytical workstreams, advises executive clients, guides cross-functional teams, supports transformation roadmaps, and promotes responsible AI use. The role also shapes thought leadership, business growth, operational improvement, and delivery quality across payer advisory engagements.

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 a marketing data science team developing and deploying machine learning models for customer acquisition and personalization. Responsibilities include designing, training, validating, evaluating, and implementing models; analyzing large-scale structured and textual data; developing Python libraries; collaborating with engineers, product managers, campaign managers, and analysts; and translating technical insights into business outcomes. The role also includes team leadership and talent development in a hybrid work environment.

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.