Bayesian Health, Inc. - Senior/Staff Machine Learning Data Scientist
Requirements
• Ph.D. in a relevant field plus 3+ years experience shipping ML based software products • Experience owning your ML models from prototyping to production, especially real-time algorithms that update dynamically across time • Experience writing production-grade Python and SQL code to implement and evaluate ML models in production systems • Track record of using statistics and performance metrics to compare end-to-end ML and product performance • Experience shipping breakthrough or 0-1 products from end to end, interpreting and leveraging State-of-the-Art methods to do so • Experience using messy clinical and health data to design new products for large Health Systems • Experience with any of the following: PyTorch, PySpark, HL7, FHIR, EHR, time series data, signal processing, MLFlow, anomaly detection, Bayesian statistics, quantile regression, time-series forecasting • You bring passion and enthusiasm to your work, and are excited to join a growing team to Get Stuff Done and save lives!
Responsibilities
• As a Senior/Staff Machine Learning Data Scientist, you are not satisfied with training and tuning ML models that predict clinical conditions in patients; you also want to own the effectiveness of your model in the real world. In practice, that means you aren’t afraid to get your hands dirty by writing data mapping code, debugging a specific patient case by following patient data as it moves through our AWS services, or improving the timeliness of your model’s predictions by reading and writing production-grade Python and SQL code. • Model Prototyping: Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods • Model Prototyping: • Productionizing: The same models that you develop with production-grade Python • Productionizing: • Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploy production-grade Python code to implement those strategies • Deploying: • Cross-Functional Alignment: Data Science for storytelling – understand model performance and metrics, and present this to technical and non-technical users, both internally and externally • Cross-Functional Alignment
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