Data Scientist – Clinical AI

Role Overview

• Translate medical policy into executable logic
• Read and interpret medical policies and clinical criteria (e.g., lab thresholds, temporal windows, trend logic, exclusions).
• Convert requirements into correct, maintainable SQL and Python implementations (e.g., creatinine-based AKI rules, bilirubin thresholds, troponin dynamics, ABG-derived criteria).
• Design rule representations that are composable and auditable (clear inputs, outputs, assumptions, edge cases).
• Prompt engineering and system parameter tuning for AI configuration that extracts clinical information from medical records.
• Build robust clinical feature pipelines.
• Create and maintain pipelines that compute clinical features from extracted signals (labs, vitals, flowsheets, notes-derived facts).
• Handle tricky realities: missing timestamps, multiple measurement sources, unit normalization, deduplication, conflicting values, provenance tracking.
• Own measurement, evaluation, and continuous quality improvement
• Define and instrument accuracy metrics for the AI system that extracts data from medical records.
• Build gold datasets, sampling strategies, and review workflows with clinical/operations partners.
• Perform error analysis, identify root causes (retrieval failures, OCR issues, extraction ambiguity, policy interpretation gaps), and drive improvements.
• Establish engineering frameworks and tooling
• Create reusable tooling for policy-to-code translation: templates, test harnesses, validation suites, regression checks, and monitoring dashboards.
• Improve infrastructure for large-scale runs: orchestration, logging, lineage, versioning, and reproducibility.
• Implement guardrails and QA gates so policy logic changes are safe, traceable, and measurable.
• Partner deeply with domain experts
• Work with clinicians, policy specialists, and operations to clarify ambiguous requirements and ensure implementations reflect real-world intent.
• Produce clear documentation that explains what the code is doing and why, with examples and edge-case handling.

Requirements

• Strong SQL and Python engineering skills
• Ability to translate nuanced requirements into correct SQL (CTEs, window functions, joins at scale, performance tuning) and production-quality Python.
• Experience operationalizing rules + models
• Track record of implementing complex business/clinical logic and deploying it reliably.
• Comfort working with imperfect, messy, high-volume datasets.
• Evaluation/Metric mindset
• Experience designing metrics, building ground truth, running experiments, and improving system quality through structured iteration.
• Systems thinking and rigor
• You build frameworks that make other engineers/scientists faster: shared libraries, patterns, tooling, and clear interfaces.
• You sweat details: edge cases, provenance, temporal logic, unit conversions, and regression safety.
• Healthcare curiosity (and willingness to learn fast)
• Interest in medical records, clinical data, and how policies translate into decision criteria.

Tech Stack

• Python
• SQL

Benefits

• Work from anywhere in the US! Machinify is digital-first.
• Top Medical/Dental/Vision offerings
• FSA/HSA
• Tuition reimbursement
• Competitive salary, 401(k) with company match
• Unlimited PTO
• Additional health and wellness benefits and perks
• Flexible and trusting environment where you’ll feel empowered to do your best work