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Waymark is a mission-driven healthtech company transforming care delivery for Medicaid patients through technology-enabled, human-centered support. The company partners with communities to remove barriers in healthcare access and reimagine what's possible in Medicaid delivery.
As a Data/AI Scientist II, you will contribute to building machine learning and AI models that directly support care teams—including community health workers, social workers, pharmacists, and care coordinators. Your work will focus on claims data, electronic health records (EHR), and care worker data to enable risk stratification, care gap prediction, and LLM-based clinical decision support tools.
Key responsibilities include:
- Build and validate ML/AI models on claims and EHR data for care delivery use cases, working under guidance of senior data scientists
- Support development of LLM-based tools and AI applications integrated into care team workflows
- Develop and maintain production Python code for data processing, model development, and ML pipelines following software engineering best practices (version control, code review, testing)
- Collaborate with engineering, product, and analytics teams to translate requirements into technical solutions
- Develop healthcare subject matter expertise in data structures, quantitative methods, and healthcare data science applications
Minimum qualifications: Master's degree in Data Science, Computer Science, Statistics, or related field; 3+ years hands-on Python experience (demonstrated via GitHub or portfolio); solid foundations in statistical and machine learning methods including classical ML and modern AI/LLM applications; strong SQL skills and experience with large, messy real-world datasets; Git proficiency and team collaboration experience; strong cross-functional communication skills.
Preferred: 1-2 years industry data science experience; exposure to healthcare data through coursework, research, or work; familiarity with ML experimentation practices (experiment tracking, model validation, reproducible workflows); genuine curiosity about healthcare and technology intersection.
