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Bayesian Health

AI-powered clinical data insights platform

Senior/Staff Machine Learning Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listed

About the Job

Requirements

  • A Ph.D. in a relevant field and at least 3 years of experience shipping machine-learning-based software products.
  • Experience owning machine-learning 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 machine-learning models in production systems.
  • A track record of using statistics and performance metrics to compare end-to-end machine-learning and product performance.

Responsibilities

  • Develop and tune new machine-learning models and labeler systems based on a deep understanding of clinical use cases and state-of-the-art machine-learning methods.
  • Productionize developed models using production-grade Python.
  • Identify strategies to improve production machine-learning-based systems, then write, debug, and deploy production-grade Python code to implement those strategies.
  • Understand model performance and metrics, and present them to technical and non-technical users, internally and externally.

Desired Qualifications

  • Experience shipping breakthrough or 0-to-1 products end to end and 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 PyTorch, PySpark, Health Level Seven (HL7), Fast Healthcare Interoperability Resources (FHIR), electronic health records (EHR), time-series data, signal processing, MLflow, anomaly detection, Bayesian statistics, quantile regression, or time-series forecasting.

About the Company

Bayesian Health

AI-powered clinical data insights platform

Bayesian Health uses AI to help hospitals turn raw healthcare data into real-time, actionable insights for clinicians. Its platform continuously analyzes patient data and care workflows to support proactive, higher-quality care, especially for patient outcomes. How it works: a subscription-based platform that integrates with existing hospital systems, processes real-time data, and presents clinicians with actionable insights. It also provides ongoing updates, training, and customization to fit each provider’s needs. How it stands out: it combines rigorous scientific methods with practical AI tools, emphasizes real-time data analysis and proactive care, and offers strong integration and support typical for biotech-grade solutions. Its goal is to improve patient outcomes by enabling care teams to act on timely, data-driven insights inside hospitals.

Company size
11-50
Company stage
Late Stage VC
Total funding
$30.2M
Headquarters
New York City, New York
Founded
Not listed

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