EntityRisk is looking to optimize risk-sharing, advance market access, improve evidence-generation investments, and correctly assess commercial value by predicting the clinical, social, and commercial value of new medical technologies in the real world
Requirements
- 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)
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
Other
- MEng, MS, or PhD in a quantitative field (e.g., statistics, economics, epidemiology, engineering, mathematics, finance, computer science)
- Good written and verbal communication skills
- Collaborative and intellectually curious with a desire to expand their skills and knowledge
- 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
