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

Your Profile

• You have 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
• You have strong foundations in applied statistics and ML — regression, classification, forecasting, clustering, experimental design, and model evaluation — and you know when each is the right tool.
• You instinctively build using agentic tools (Claude Code, Codex, etc) and are invested in pushing the boundaries of what is possible with agentic development
• You can translate technical recommendations and model behavior clearly and concisely for non-technical product, commercial, and customer audiences
• You have hands-on experience merging and blending messy, real-world datasets — time-series, geospatial, demographic, etc — and thrive on extracting signal from noise
• You are comfortable working in modern cloud data warehouses with SQL to prepare data for modeling, and can collaborate effectively with data engineers on production pipelines
• You are fluent in Python’s data and ML stack and opinionated about your preferred approaches, techniques, or model implementations 
• Are you interested in this position?

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