Top Hybrid Data Science Jobs in Boston, MA

The Data Science Intern will build predictive models, analyze customer data, and design AI-driven frameworks to improve customer health and retention. This role focuses on hands-on projects involving complex datasets and insights generation.

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.

The Senior Data Scientist will develop advanced analytics to enhance product value, mentor junior members, and lead initiatives in healthcare commercialization.

The role involves leveraging data analytics and AI techniques to support client needs, developing models, managing data teams, and building AI solutions. Key responsibilities include data processing, client collaboration, and presenting 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.

The Senior NGS Bioinformatician will lead NGS data analysis, product development, and statistical studies for various cancer and rare disease projects.

The Senior Data Scientist will engage with business partners to solve problems using data science, lead model development, and mentor others, focusing on supply chain and causal modeling.

The Director of Data Science leads data science and machine learning initiatives for Commerce Media, translating complex problems into scalable solutions, mentoring teams, and communicating insights effectively across functions.

As a Senior Data Scientist, you'll build scalable models to improve logistics and enhance user experiences, collaborating with product managers on high-value problems.

Lead design and deployment of graph ML and network-biology analyses to generate drug target hypotheses from RWD and multi-omic data. Prototype methods, integrate solutions with engineers, prioritize tasks, and communicate results to cross-functional stakeholders while championing data science best practices.