Staff Data Scientist — Healthcare ML & Production Models

MedBridge Inc.; Yuma (AZ); A distancia; USD 190.000 - 220.000; Jornada completa

Descripción de la vacante

Medbridge is a remote-first SaaS company serving large healthcare providers. The Staff Data Scientist role owns predictive modeling end-to-end—framing problems with product and clinical partners, selecting methods, building and validating models, and deploying to production in Snowflake.

You’ll monitor, retrain, and measure impact, influencing the Data Science practice. You will lead modeling standards, ensure reproducible pipelines, and communicate uncertainty and tradeoffs clearly to product,

Formación

• Proven production model experience and deployment ownership.
• Strong statistical depth and uncertainty quantification.
• Experience with healthcare data and evaluating models in production.

Responsabilidades

• Own design, development, validation, and production operation of predictive models.
• Set methodological standards and document decisions for stakeholders.
• Write production-quality code with testing and reproducible pipelines.
• Assess impact of Medbridge products on patient outcomes using experimental/observational methods.
• Collaborate with product, engineering, clinical, and customer-facing teams to ship models.

Conocimientos

Python

SQL

Software engineering

Testing

Version control

Code review

Reproducible pipelines

Causal inference

Educación

Quantitative degree (Statistics/Biostatistics/CS/Economics)

Herramientas

Snowflake

Descripción del empleo

Medbridge is a remote-first SaaS company serving large healthcare providers. The Staff Data Scientist role owns predictive modeling end-to-end—framing problems with product and clinical partners, selecting methods, building and validating models, and deploying to production in Snowflake.

You’ll monitor, retrain, and measure impact, influencing the Data Science practice. You will lead modeling standards, ensure reproducible pipelines, and communicate uncertainty and tradeoffs clearly to product,