Top Senior Data Science Jobs in Boston, MA

Lead the full lifecycle of data science models that predict and optimize advertising performance across channels. Define learning targets, build training data, develop forecasting and causal measurement systems, design experiments, validate models, and translate outputs into customer-facing recommendations. Partner with product, engineering, data, and customer teams to deploy models, assess uncertainty and drift, and demonstrate measurable business impact.

Analyze crop protection and sensor data, develop statistical and machine learning models, build dashboards and recommendation engines, improve perception pipelines, establish data quality measures, and communicate findings across technical and agricultural teams.

Lead experimentation and decision science initiatives from problem definition through recommendations. Design and analyze A/B tests, quasi-experiments, matched-market tests, and causal inference studies. Apply advanced statistics, regression, predictive modeling, machine learning, and big-data analysis using Python, R, SQL, and PySpark. Partner with stakeholders, communicate actionable findings, build reproducible workflows, and mentor analysts and junior data scientists.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Lead the end-to-end development, validation, deployment, and productionization of large-scale predictive models. Conduct advanced data-driven research, solve ambiguous business problems, and translate business goals into analytical frameworks. Partner with senior stakeholders and engineering leaders to deliver strategic insights, support planning and product innovation, communicate complex findings, and mentor data scientists while maintaining rigorous technical and engineering standards.

Build, validate, deploy, monitor, and maintain predictive models and analytical reports. Translate findings for technical and non-technical stakeholders, promote data literacy, deliver training, collaborate on data infrastructure, apply advanced statistical and machine learning methods, and support analytical requests across mental health and crisis care programs.

Partner with Marketing as the embedded data science lead to build marketing measurement, attribution, LTV/ROAS/CAC models, run experiments, analyze the full funnel, create forecasts and dashboards, and influence channel strategy and budget allocation.

As the Staff Data Scientist, you will lead the data science function, driving strategy, mentoring peers, and shaping product direction through advanced analytics and experimentation.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Build and deploy machine learning and experimentation systems for personalization, ranking, targeting, attribution, and causal measurement. Develop features and pipelines from transaction and behavioral data, design A/B tests, productionize models with engineering teams, and improve recommendation systems. Translate analytical results into business insights tied to conversion, retention, lift, and ROI while owning projects from problem framing through post-launch evaluation.

Develop and enable clinical data assets for analytics and AI/ML use cases. Responsibilities include designing clinical data models, taxonomies, feature stores, documentation, quality frameworks, dashboards, queries, and reusable datasets. Partner with clinical, business, engineering, and analytics stakeholders; support data governance and HIPAA compliance; translate clinical concepts into analytical frameworks; and train data consumers. Requires expertise in healthcare datasets, coding systems, SQL, Python, cloud platforms, and clinical informatics.

Analyze large, diverse datasets (claims, hazard, geospatial) and apply advanced data science, ML, and AI to model catastrophe, hazard, exposure, and man-made risks. Evaluate vendor models, develop internal risk views and tools, collaborate with domain experts, and support underwriting decision-making and account-specific risk analyses.

Lead end-to-end predictive analytics and machine learning initiatives, including forecasting, churn prediction, and risk modeling. Translate business needs into scalable ML solutions, build and deploy models on AWS SageMaker, develop production ML pipelines, and oversee monitoring, governance, explainability, and retraining. Collaborate with engineering and MLOps teams, communicate insights to stakeholders, and mentor junior data scientists while providing hands-on technical leadership.

Design and analyze incrementality tests, media mix models, causal analyses, and advanced analytics for marketing measurement. Work with large marketing datasets, assess methodological validity, conduct power and sensitivity analyses, and translate findings into media planning recommendations. Collaborate cross-functionally, improve measurement standards, and mentor junior data scientists.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and influence product, growth, finance, and operations leaders to drive measurable outcomes.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and delivering measurable business impact.

Develop and deploy predictive and causal models for personalization, retention, monetization, and product strategy. Design experimentation and measurement frameworks, analyze user and marketing behavior, quantify business impact, and translate complex findings into strategic decisions. Collaborate with Product, Engineering, Marketing, and Data Product teams to build scalable data solutions. Mentor peers, promote rigorous model validation and governance, and apply Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to production-ready data science initiatives.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

Lead the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile practices.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

Build production AI retrieval and knowledge systems for rare disease research at NIH. Model biomedical ontologies in PostgreSQL, develop semantic search, retrieval-augmented services, ranking, evaluation, and observability, and deliver user interfaces with Next.js, React, and TypeScript. Deploy and operate services in on-premises and HPC Kubernetes environments, including GPU inference. Collaborate with NIH staff, clinicians, and researchers, and contribute to scientific publications.

Build and operate agentic AI systems for rare disease research, including multi-turn workflows, structured and validated outputs, tool calling, prompt testing, evaluation suites, asynchronous execution, streaming, observability, and failure handling. Collaborate with NIH staff, clinicians, geneticists, researchers, and patient communities to translate needs into reliable data models and system behavior. Own components through deployment, establish AI engineering standards, and contribute to scientific manuscripts, abstracts, and conference posters.

The Principal Data Scientist - Oncology will lead efforts in standardizing and connecting biomedical and clinical data through the development of scalable knowledge graph infrastructure and AI-Ready datasets.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.