Data Scientist (Engineering)

About The Role

EntityRisk's platform predicts the clinical, social, and commercial value of new medical technologies in the real world to help optimize risk-sharing, advance market access, improve evidence-generation investments, and correctly assess commercial value.

We are looking for a Data Scientist with strong engineering skills to join the Data Science & Engineering (DSE) team, which develops and implements EntityRisk's core methodology and software.

In this role, you will help create the next generation of the platform by designing domain-specific languages, automation pipelines, and user-facing tools that enable health-economic and probabilistic ML workflows to be built, configured, and deployed at scale. Unlike typical ML or data sciences roles, you won't just train or deploy models, you'll design and implement general abstractions, workflows, and infrastructure for model development, implementation, and presentation.

Ideal candidates are collaborative and intellectually curious with a desire to expand their skills and knowledge. Successful candidates will have good written and verbal communication skills in addition to strong technical skills.

Responsibilities

• Implement core platform capabilities, including domain-specific languages, automation pipelines, and user-facing tools for health-economic and probabilistic ML workflows
• Implement modular functions and classes to support generic but automated model development, deployment, and presentation at scale
• Collaborate with health economics, statistics, and ML experts to translate client and research needs into engineering solutions
• Contribute to design discussions by providing practical input on implementation feasibility, usability, architecture trade-offs, and automation
• Integrate domain specific languages into external products, ensuring scalability, maintainability, and high performance
• Participate in code reviews, testing, and documentation to maintain high engineering standards

Qualifications

• MEng, MS, or PhD in a quantitative field (e.g., statistics, economics, epidemiology, engineering, mathematics, finance, computer science)
• Fluency in Python, including writing reusable, maintainable, and well-tested code
• Professional experience with version control (Git preferred), unit testing, code reviews, and documentation best practices
• Experience building automated pipelines or scripts that execute configurable workflows
• Exposure to machine learning, probabilistic statistical modeling, and/or computational simulation (can be academic or applied)
• Ability to translate technical work into clear explanations for cross-functional teams and contribute effectively to design discussions
• Curiosity and willingness to learn new technologies, frameworks, or domain knowledge
• For Senior Data Scientist: 2-4 years of relevant work experience and software design aptitude