Top Data Science Jobs in Boston, MA

MORSE Corp seeks a Data Scientist experienced in data analysis, algorithm development, and machine learning for national security solutions. Responsibilities include evaluating algorithms, transitioning them into software applications, and presenting findings to stakeholders.

As a Data Scientist, you will design and operationalize advanced analytics, develop scalable models, and collaborate with teams to enhance product capabilities in healthcare.

Lead end-to-end data science projects, develop machine learning models, collaborate with stakeholders, mentor team members, and communicate insights effectively.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Work with clients and engineers to audit marketing datasets, build analytics pipelines, and apply predictive models (segmentation, attribution, propensity) using SQL, Python, cloud big-data tools, and ML frameworks. Support implementations, develop analytic applications, and communicate findings to drive marketing outcomes.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

The Data Scientist will analyze user behavior, create segmentation strategies, design experiments, and track business metrics to drive growth in a SaaS company.

As a Senior Data Scientist, you will develop technologies for signals exploitation, perform data analysis, and implement algorithms while collaborating with project teams.

The Data Scientist II will lead RWE studies, derive insights from real-world clinical data, and contribute to methodologies using advanced statistical techniques and AI tools, collaborating closely with pharmaceutical partners.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

As a Data Scientist, you will develop algorithms, collaborate on product requirements, and communicate data-driven insights, while enhancing product offerings and delivering actionable insights.

Lead Data Scientist responsible for guiding teams in data analysis, algorithm development, and AI application to address national security challenges. Engage stakeholders and oversee algorithm testing and evaluation.

Perform quantitative real estate and urban economic analysis, applying econometric and spatial methods to synthesize property, macroeconomic, and geospatial data. Build and maintain automated data pipelines, ensure data integrity, document dataflows, and prototype ML/AI methods. Collaborate with research, GIS, and engineering teams to produce reproducible insights and analytical products for stakeholders.

Design and validate statistical and predictive models on large healthcare datasets to uncover cost and utilization drivers. Produce visualizations and actionable recommendations, lead cross-functional analytic projects, and partner with underwriting, product, contracting, sales, and finance to inform pricing, forecasting, and product strategy.

Design and implement quantitative behavior metrics for autonomous vehicles, build and maintain data pipelines and dashboards, analyze large-scale vehicle logs and simulation data to identify regressions, set metric thresholds to support geofence expansion, and communicate findings to technical and non-technical stakeholders.

Embedded within a core business area, you will define analytical problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-driven recommendations end-to-end to influence product, growth, AI, or partnerships.

Embed on a core business team to define problems, build metrics, run analyses and experiments, and drive data-informed product, growth, AI, or partnership decisions end-to-end. Surface insights, recommend actions, and own execution to improve key business metrics.

Embed within a core business area to own analytics end-to-end: define problems, build metrics/reports/experiments, analyze data, surface insights, and drive outcome-focused recommendations with stakeholders to improve growth, product, AI/automation, or partnerships.

Embed with a business unit to define problems, build metrics and analyses, run experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded on a core business team, the Data Scientist defines problems, builds metrics and reports, runs analyses and experiments, and translates insights into executable recommendations to drive growth, product, AI/automation, or partnership outcomes.

Embed within a business unit to define problems, build metrics/reports/experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

The role involves providing computational and bioinformatics support for RNA therapeutics, developing data analysis pipelines, and collaborating with experimental scientists.

Develop and deploy analytics applications and predictive models using machine learning, NLP, and statistical methods on large healthcare datasets. Build data architectures, ETL pipelines, MLOps practices, visualizations, and support model deployment and monitoring to inform business and clinical decisions.

Develop and implement analytics applications and predictive models using machine learning, NLP, and big-data tools. Source and profile healthcare data, build scalable ingestion pipelines, evaluate models, visualize results, and support deployment across channels to inform clinical and business decisions (pharmacy pricing, medical costs, risk scoring, customer engagement).

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.