Top Data Science Jobs in Boston, MA

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 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.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

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 using complex clinical and molecular data, observational study design, survival analysis, causal inference, and machine learning. Collaborate with pharmaceutical partners, derive clinical endpoints, interpret results, develop reusable analytical code, and apply LLM-based tools to accelerate workflows. Communicate findings to technical and non-technical stakeholders through reports, presentations, manuscripts, and conference materials while partnering cross-functionally to improve data quality and analytical practices.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Conducts advanced quantitative research on aging biology using multi-omic and DNA methylation data. Responsibilities include data harmonization, statistical and machine learning analyses, biomarker validation, reproducible pipeline and software development, visualization, study design, manuscript preparation, and scientific communication. The role supports observational and interventional aging studies and explores AI-assisted data harmonization and foundation-model approaches.

Own end-to-end analytical support for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong SQL or Python skills, independent data interpretation, first-principles problem solving, clear communication, and ownership of ambiguous business challenges.

Own end-to-end analytical functions for growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; surface insights; and influence stakeholders toward decisions that improve key business outcomes. The role requires independent data work, strong problem-solving, persuasive communication, and ownership of ambiguous problems.

Own end-to-end analytical support for a business unit in growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems using SQL and Python.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, establish metrics, build reports, analyze data, design experiments, communicate insights, and drive decisions with cross-functional stakeholders. The role requires strong analytical judgment, independent execution, first-principles thinking, and persuasive communication.

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.

Build and deploy machine learning and statistical models across the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. Partner with data scientists, software engineers, and product managers; analyze large-scale numeric and textual datasets; conduct model design, training, evaluation, validation, and implementation; and translate complex analyses into business outcomes.

Develops meteorological data visualizations, statistical analyses, prototypes, and validation processes for satellite, NWP, and AI weather models. The role partners with scientific, engineering, product, marketing, and customer teams to troubleshoot data and model performance, strengthen use cases, and communicate weather insights. Responsibilities include working with proprietary satellite data, operationalizing AI-assisted scientific workflows, integrating diverse meteorological formats, and supporting customer-focused applications of weather intelligence.

The Data Scientist will analyze complex healthcare datasets, build Python-based reporting pipelines, clean and validate data, and create tables, visualizations, and PowerPoint presentations. They will identify cost and utilization drivers, answer account questions, improve client-facing analytics, and collaborate with underwriting, medical management, sales, finance, and other business partners. The role requires translating complex analyses into actionable insights while managing multiple projects and deadlines.

Develop forecasts, machine learning models, and optimization algorithms for renewable energy and battery storage assets. Analyze large energy-market datasets, build simulations, deploy production-grade data science workflows, create dashboards, and establish asset-optimization metrics. The role collaborates with engineering, operations, and energy markets teams to support real-time operations, market strategies, and business development.

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.

Own Junction’s data science research, statistical modeling, experimentation, and evaluation strategy across diagnostics, clinical workflows, and AI-enabled products. Build predictive and decision-support models, establish measurement and quality frameworks, partner with engineering to deploy reliable systems, and communicate findings to technical and executive stakeholders. The role combines strategic roadmap ownership with hands-on analytical work in a high-ambiguity startup environment.

Develop and deploy statistical and machine learning models, conduct exploratory analysis, forecasting, segmentation, experimentation, and AI initiatives. Translate business needs into analytical frameworks, KPIs, predictive insights, and recommendations. Collaborate with data engineering, BI, QA, and business teams to ensure data readiness, model integration, quality, governance, and auditability. Support revenue, marketing, financial, and operational decision-making through scalable analytical solutions.