Responsibilities

Design, train, and validate predictive and statistical models that turn noisy healthcare data into reliable intelligence products used by MedTech commercial teams
Frame open-ended business questions as modeling problems — selecting the right approach (classification, regression, clustering, causal inference, ensembles, etc), defining success metrics, and quantifying uncertainty
Engineer features and conduct applied research across time-series, geospatial, demographic, insurance claims, and more datasets, to improve the coverage and signal quality of our core data assets
Own the full model lifecycle: exploratory analysis, baseline modeling, experimentation, validation, deployment, and post-launch monitoring for drift and performance
Partner with product managers and cross-functional stakeholders to translate customer problems into model-backed product features and to shape the roadmap
Provide technical leadership and mentorship on statistical and ML methodology for engineers and analysts across the Data organization and across all of AcuityMD
Document models, assumptions, and data contracts so results are interpretable and reproducible for internal and external audiences

Requirements

6+ years of experience in machine learning roles building and shipping statistical or machine learning models into a production environment, ideally as part of product teams delivering to external customers
Strong foundations in applied statistics and ML — regression, classification, forecasting, clustering, experimental design, and model evaluation
Build using agentic tools (Claude Code, Codex, etc) and invested in pushing the boundaries of what is possible with agentic development
Translate technical recommendations and model behavior clearly and concisely for non-technical product, commercial, and customer audiences
Hands-on experience merging and blending messy, real-world datasets — time-series, geospatial, demographic, etc — and thrive on extracting signal from noise
Comfortable working in modern cloud data warehouses with SQL to prepare data for modeling, and collaborate effectively with data engineers on production pipelines
Fluent in Python's data and ML stack and opinionated about preferred approaches, techniques, or model implementations

Experience with healthcare datasets, such as medical insurance claims, prescriptions, EHR/EMR, lab test results, or patient demographic data