Top Hybrid Data Science Jobs in Boston, MA

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.

Leads strategy, development, deployment, and scaling of AI-powered intelligence products for insurance underwriting. Partners with business leaders, data scientists, engineers, architects, and technology teams to define roadmaps, prioritize investments, deliver machine learning and Generative AI solutions, and drive adoption. Oversees product valuation, governance, monitoring, responsible AI, stakeholder alignment, and measurable business outcomes across underwriting segments and lines of business.

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 teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

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.