Top Hybrid Data Science Jobs in Boston, MA

Lead data science strategy for autonomous vehicle fleet and simulation data. Build statistical, predictive, and generative models to assess safety and performance across operating conditions, translate telemetry into launch-readiness metrics, support rapid operational investigations, and advise autonomy and systems teams. Mentor and scale a four-person team while communicating analytical findings to engineers and executives.

Develop and evaluate AI and machine learning methods for low-molecular-weight drug discovery. Build generative, multimodal, foundation, and agentic models; establish rigorous benchmarking; connect model performance to experimental decisions; and integrate AI into design-make-test-analyze workflows. Collaborate with medicinal chemistry, DMPK, biology, engineering, and data science teams while communicating results to multidisciplinary stakeholders.

Provides strategic leadership for enterprise data science, analytics governance, automation, and data enablement across therapeutic areas. Oversees analytics pipelines, data architectures, visualization, AI/ML adoption, regulatory compliance, and clinical data standards. Advises executives, leads cross-functional collaboration, manages analytics teams, drives organizational change, and advances scalable analytical capabilities. Requires extensive experience with large datasets, programming, statistics, data visualization, database concepts, and people management in clinical or digital health settings.

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.

Develop and deploy production-grade AI, machine learning, and generative AI solutions for insurance business challenges. Responsibilities include building LLM applications, prompt engineering, RAG workflows, data pipelines, experiments, proof-of-concepts, and scalable AI systems. The role involves monitoring model performance, generating business insights, collaborating with stakeholders, and communicating technical findings to diverse audiences.

As a Data Scientist, you will advance applied ML capabilities for healthcare, develop LLM-based chart abstraction techniques, and enhance data science best practices.