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Company Overview
A high-growth AI and data company is transforming the MedTech industry by helping medical technology companies understand product usage, identify opportunities for physicians, and deliver cutting-edge healthcare solutions. The organization leverages agentic AI development practices to convert complex, real-world healthcare data into actionable intelligence, accelerating product adoption and improving patient outcomes. With backing from leading investors, the company values extreme ownership, collaboration, and innovation in building data-driven solutions.
Summary
The Senior Machine Learning Engineer will lead the evolution of core healthcare data assets by designing, building, and deploying predictive and statistical models. This role focuses on converting messy, real-world health data into reliable insights that drive MedTech product adoption and improve patient care. The position involves cross-functional collaboration with engineering, product, and commercial teams, and requires strong technical leadership in machine learning methodologies.
Responsibilities
Design, train, and validate predictive and statistical models to transform noisy healthcare data into actionable intelligence products
Frame open-ended business problems as modeling challenges, selecting the appropriate ML/statistical approach and defining success metrics
Engineer features and conduct applied research across diverse datasets including time-series, geospatial, demographic, and insurance claims data
Manage the full model lifecycle: exploratory analysis, baseline modeling, experimentation, validation, deployment, and post-launch monitoring
Partner with product managers and cross-functional teams to translate customer problems into model-backed product features and roadmap priorities
Provide technical leadership and mentorship on ML/statistical methodology to engineers and analysts across the organization
Document models, assumptions, and data contracts for interpretability and reproducibility
Qualifications
6+ years of experience building and shipping statistical or machine learning models in production environments, preferably on product teams delivering to external customers
Strong foundations in applied statistics and ML, including regression, classification, forecasting, clustering, experimental design, and model evaluation
Hands-on experience with messy, real-world datasets (time-series, geospatial, demographic, etc.) and extracting signal from noise
Proficiency in Python’s data and ML stack, SQL, and cloud data warehouses
Ability to communicate technical recommendations and model behavior to non-technical stakeholders
Experience in agentic development tools and workflows is highly valued
Experience with healthcare datasets such as medical insurance claims, prescriptions, EHR/EMR, lab results, or patient demographics is a strong plus
Disclaimer
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