Applied Scientist, AI

Job Summary

Required Qualifications

• •MS or PhD in computer science, statistics, machine learning, applied math, operations research, biomedical informatics, epidemiology, or a related quantitative field
• •Exceptional applied experience that substitutes for formal graduate training
• •Depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI
• •Experience working with healthcare data such as claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement data
• •Experience working with PHI, HIPAA-aware systems, or other sensitive regulated data
• •Experience collaborating with clinicians, clinical operations teams, or other high-stakes domain experts
• •Experience in a startup or fast-moving applied environment where ambiguity, speed, and rigor all mattered

Desired Qualifications

• •Shipped models that reached production and had measurable real-world impact
• •Knowledge of when traditional ML approaches are likely to outperform LLMs, and when LLMs are the right tool
• •Understanding of how ML models work under the hood and can explain them clearly to non-technical stakeholders
• •Focus on impact and knowing that the simplest model is often the best one
• •Treating evaluation as one of the most important parts of model development
• •Noticing when a metric is misleading, incomplete, or disconnected from real-world outcomes
• •Catching leakage, bias, and confounding that others miss
• •Moving fluidly between modeling, error analysis, stakeholder partnership, and production handoff
• •Comfort with ambiguity and ability to adapt modeling approaches to problems that do not come with a playbook
• •Balancing scientific rigor with the practical need to ship useful systems
• •Experience using AI coding assistants such as Claude Code, Cursor, or similar tools as part of your development workflow
• •Built, evaluated, and iterated on machine learning or AI models for real-world use cases
• •Turned ambiguous business, product, clinical, or operational problems into measurable modeling tasks
• •Designed rigorous offline evaluations, experiments, or analyses that informed production or product decisions
• •Worked with messy real-world datasets where labels, outcomes, and causal relationships are imperfect
• •Used statistical reasoning, experimental design, and error analysis to understand model performance
• •Built models using Python and standard ML or AI tooling such as PyTorch, scikit-learn, NumPy, pandas, Polars, Hugging Face, Matplotlib, or similar
• •Compared modeling approaches and made pragmatic decisions about when to use traditional ML, LLMs, heuristics, or simpler baselines
• •Communicated model performance, limitations, tradeoffs, and uncertainty to technical and non-technical stakeholders
• •Partnered with engineering, product, data, operations, clinical, or domain experts to move models closer to production impact
• •Operated with enough engineering depth to run experiments end to end and self-serve deployments or production handoffs when needed