Applied Scientist, AI

What you will do

• Turn ambiguous healthcare, product, and operational problems into well-posed ML, AI, ranking, optimization, NLP, or LLM-based tasks
• Build strong baselines and improve on them efficiently using the right modeling approach for the problem
• Develop models across traditional ML, deep learning, NLP, and LLM-based approaches where appropriate
• Design offline and online evaluations that are honest, measurable, and predictive of real-world impact
• Choose metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomes
• Run careful error analysis and use it to improve model quality, product fit, and operational usefulness
• Identify label leakage, selection bias, confounding, and other data artifacts before they reach production
• Explore messy real-world data, assess label quality, and determine whether a problem is ready for modeling
• Partner with ML engineering to productionize models reliably and define what production-readiness requires
• Work with clinical stakeholders and subject-matter experts to validate assumptions, review model errors, and understand edge cases
• Explain model tradeoffs, uncertainty, limitations, and expected impact clearly to product, operations, clinical, and leadership teams
• Write experiment docs, summarize findings, and help teams make informed decisions about when and how to deploy AI systems
• Pressure-test whether results are real, robust, and useful before recommending production use

What you have done

• 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
• Used AI coding assistants such as Claude Code, Cursor, or similar tools as part of your development workflow

What gives you an edge

• You have an MS or PhD in computer science, statistics, machine learning, applied math, operations research, biomedical informatics, epidemiology, or a related quantitative field
• You have exceptional applied experience that substitutes for formal graduate training
• You have depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI
• You’ve shipped models that reached production and had measurable real-world impact
• You’ve worked with healthcare data such as claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement data
• You have experience working with PHI, HIPAA-aware systems, or other sensitive regulated data
• You know when traditional ML approaches are likely to outperform LLMs, and when LLMs are the right tool
• You have experience collaborating with clinicians, clinical operations teams, or other high-stakes domain experts
• You’ve worked in a startup or fast-moving applied environment where ambiguity, speed, and rigor all mattered

What makes you successful

• You understand how ML models work under the hood and can explain them clearly to non-technical stakeholders
• You focus relentlessly on impact and know that the simplest model is often the best one
• You treat evaluation as one of the most important parts of model development
• You notice when a metric is misleading, incomplete, or disconnected from real-world outcomes
• You catch leakage, bias, and confounding that others miss
• You move fluidly between modeling, error analysis, stakeholder partnership, and production handoff
• You can hand a model to engineering and explain its limits to a clinician with equal clarity
• You are comfortable with ambiguity and can adapt modeling approaches to problems that do not come with a playbook
• You balance scientific rigor with the practical need to ship useful systems