Top Principal Data Scientist Jobs in Boston, MA

Lead the integration of advanced imaging analytics and AI in drug development, focusing on medical image analysis and clinical trials.

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

Lead high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

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.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Lead GTM data science for growth, marketing, lifecycle, and personalization. Design, build, and deploy production ML models and decision systems (acquisition, retention, LTV, personalization), own MLOps and feature pipelines, measure experiment/causal impact, mentor others, set technical direction, and drive cross-team execution to improve customer experience and business outcomes.

Lead strategy and technical direction for generative and multimodal vision systems. Build end-to-end model workflows, improve realism, controllability, and robustness, drive evaluation and production-readiness, partner with engineering to deploy optimized inference, and mentor scientists while owning a major technical pillar from research to production.

Design, build, validate, and deploy machine learning models across the credit card lifecycle. Partner with cross-functional teams, analyze large numeric and textual datasets using Python, Spark, H2O and cloud platforms, and translate technical insights into business impact for card products.

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.

Lead and execute Real-World Evidence (RWE) studies end-to-end: design, data feasibility, extraction, cleaning, statistical analysis, programming, and interpretation. Develop shared tools and best practices, mentor team members, produce protocols and analysis-ready deliverables, and support regulatory interactions alongside therapeutic area scientists.

Lead end-to-end data science initiatives for commercial property or homeowners insurance: acquire and prepare data, build and validate predictive models, deploy and monitor production models, ensure governance and documentation, advise teams, and evaluate emerging ML/AI techniques to improve pricing, risk selection, and underwriting outcomes.

As a Senior Data Scientist at Yahoo Mail, you will enhance user growth strategies via product innovation, overseeing research projects and analytics.

Leads data science, AI, digital health, and analytics strategy for Immunology R&D. Partners with scientific, clinical, platform, and engineering teams to identify use cases, analyze workflows, evaluate emerging technologies, develop scalable solutions, and improve research execution and decision-making. The role focuses on healthcare, imaging, clinical, biomarker, real-world, and digital health data, including generative and agentic AI applications.

Develop and validate SDTM and ADaM datasets using R per CDISC standards, support TLF generation in R or SAS, perform QC and reconcile data issues, collaborate with biostatistics and clinical teams, and contribute to automation and reproducible workflows for regulatory submissions.

Lead development and productization of advanced statistical and ML solutions for clinical development. Design simulation studies, build scalable data and model pipelines, ensure model fairness/compliance, integrate models into product platforms, and drive scientific leadership and cross-functional initiatives in regulated pharma environments.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Lead Data Scientist responsible for managing projects in data engineering, model design, and infrastructure deployment while collaborating with clients to develop data-driven solutions.

The Principal Data Scientist will lead the design of knowledge graph infrastructure in Immunology, standardizing and integrating extensive biomedical data for cross-functional analytics and insights. Responsibilities include data modeling, ontology curation, and collaborating with various scientific teams.

Leads the design, development, deployment, and monitoring of advanced statistical and machine learning models for insurance pricing, underwriting, claims, fraud, retention, and growth. Advises executives and actuarial teams, establishes modeling and experimentation standards, ensures responsible and scalable productionization, and mentors data scientists. The role also advances MLOps, AI infrastructure, model lifecycle management, explainability, fairness, and data science strategy.

Lead end-to-end AI/ML initiatives to improve student retention, engagement, and enrollment. Architect and operate data pipelines, feature stores, and production ML systems; deploy, monitor, and automate models; run experimentation programing and measure business outcomes. Mentor team members and set technical standards across Product, Engineering, and CX.

Build and deploy AI/ML solutions from prototype through production for CMS, including generative AI, LLMs, RAG, intelligent classification, knowledge management, agentic frameworks, and enterprise integrations. Evaluate AI coding tools, establish AI/ML roadmaps, scale adoption, mentor data scientists, and translate technical concepts for stakeholders and leadership.

Lead development and production deployment of ML products: perform exploratory analysis, build and evaluate models, design experiments and metrics, leverage generative AI and graph-based methods, collaborate with product and engineering, influence strategy, and maintain robust CI/CD and platform practices.

Lead generative AI initiatives: define 18-24 month roadmap, develop and deploy LLMs and multimodal models for document and 3D data extraction, architect Siemens integration, drive data/ML ops, mentor scientists, and align cross-functional teams to deliver production-grade AI solutions.

Lead design and delivery of AI-ready scientific data products and integration architectures across discovery, preclinical, clinical, safety, and real-world evidence. Define metadata, data quality, lineage, and interoperability standards; partner with scientists, knowledge architects, AI engineers, and AWS platform teams; build predictive AIML models to support translational safety decision-making and enable knowledge graphs and semantic reasoning use cases.