Top Hybrid Data Science Jobs in Boston, MA

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.

The Director I, Data Science will design and evaluate generative AI and agentic systems, develop semantic layers and knowledge graphs, and build frameworks assessing AI performance, reliability, and safety. The role collaborates with data and engineering teams, leads cross-functional initiatives, promotes best practices across the data science community, advises on complex projects, and communicates technical insights to business stakeholders. This is a hands-on individual contributor position.

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

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.

Develops data integration workflows, dashboards, statistical and predictive models for biotherapeutics process development. Analyzes complex scientific and process data, identifies insights and anomalies, troubleshoots data and analysis issues, and communicates findings to technical teams. The role also supports project deliverables, external collaborations, process improvements, and training junior staff on data tools, analysis methods, and visualization practices.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

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.

Leads the strategy for scaling data science analyses used in pharmaceutical asset acquisition and diligence. Directs cross-functional Data Science and Engineering teams, determines which one-off analyses should become landscape analyses or durable tools, sets roadmaps, evaluates statistical and technical validity, identifies AI opportunities, and connects scientific findings to business impact, asset valuation, and sourcing decisions.

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.