Top Data Science Jobs in Boston, MA

Build and deploy machine learning models and scoring platforms for credit decisioning and valuation. Analyze large-scale financial and textual datasets using statistical modeling, cloud technologies, and open-source tools. Responsibilities include model design, training, validation, implementation, analytical tool development, alternative data analysis, and collaboration with software engineering and product teams to deliver scalable credit products.

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.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Leads data science initiatives for US Card acquisitions, developing and deploying machine learning models across their full lifecycle. Partners with data scientists, software engineers, product managers, and business stakeholders to solve complex problems, analyze large-scale numeric and textual datasets, validate models, and translate technical findings into business outcomes. The role also involves team leadership, talent development, model governance, monitoring, and ongoing model improvements.

Supports data science teams by cleaning and processing structured and unstructured data, engineering features, executing and monitoring machine learning pipelines, generating insights, investigating trends and risks, and collaborating with senior data scientists and business analysts. The role requires a current MIT quantitative-field bachelor's student working 10–20 hours weekly in the continental United States.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Build predictive models for churn, expansion, and look-alikes; run marketing mix and incrementality analyses; create leading indicators and cohort reporting; partner with Sales, Marketing, and RevOps to translate analytics into actionable recommendations and inform data infrastructure needs.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Build and productionize data pipelines and LLM-based, multimodal AI solutions for insurance use cases. Develop agentic AI applications using RAG, embeddings, prompting, custom tools, and evaluation frameworks. Apply responsible-AI practices, deploy and monitor machine learning systems, and lead client workshops to translate business needs into technical solutions and roadmap priorities.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, strong SQL or Python skills, persuasive communication, and ownership of ambiguous problems with measurable business impact.

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

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, SQL or Python proficiency, strong communication, and ownership of ambiguous initiatives through measurable business impact.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent data work, strong analytical reasoning, persuasive communication, and ownership of ambiguous problems through measurable business impact.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence stakeholders and leadership decisions. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems through measurable outcomes.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions and outcomes. The role requires independently structuring and interpreting data, communicating findings to leadership, and solving ambiguous problems using SQL or Python.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Develop foundation models for longitudinal wearable and physiological data, leading research from hypothesis development through modeling, experimentation, evaluation, prototyping, and product deployment. Build datasets and ML infrastructure, advance representation learning, forecasting, personalization, multimodal and generative modeling, and establish robust evaluation methods. Collaborate with scientists, clinicians, engineers, and product teams while translating research into health experiences.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Own the company-wide revenue retention and add-on monetization analytics strategy. Build causal inference and retention models covering churn, MRR, GRR, NRR, attach, and usage. Partner with Finance, RevOps, Customer Success, CPaaS, AI, and product leaders to establish governed metrics, identify opportunities, guide experimentation, and influence product, pricing, and lifecycle decisions. Set analytical standards, develop reusable methods, and provide technical direction across teams.

Develops scalable data and analytics solutions for BILL’s compliance ecosystem. Responsibilities include extracting and analyzing data, building dashboards, maintaining compliance metrics, automating workflows, troubleshooting systems, performing root-cause analysis, and supporting technology accelerators. The role requires SQL, Python, Excel, business intelligence tools, and experience with risk or compliance systems in payments or financial services.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.