Remote Staff Data Scientist – Healthcare ML & Production

MedBridge Inc.; Lorida (FL); Teletrabalho; USD 190 000 - 220 000; Tempo integral

Resumo da oferta

Medbridge is seeking a Staff Data Scientist to own end-to-end predictive models, from framing with product partners to deployment and monitoring in production. You will shape modeling standards, ensure reproducibility, and collaborate with clinical, product, and engineering teams to drive measurable patient-outcome improvements.

We value strong Python/SQL skills, experience with real-world healthcare data, and a track record of deploying models in production.

Qualificações

• Production experience with model code deployment, monitoring, and retraining.
• Strong statistical depth and uncertainty quantification.
• Experience with healthcare data and causal inference.
• Experience taking a model from an open-ended business goal to a shipped product feature.
• Clear communication with clinical, product, and leadership audiences.

Responsabilidades

• Own the design, development, validation, and production operation of predictive models.
• Set the methodological bar and document decisions for stakeholders.
• Write production-quality code and manage deployment in Snowflake with Data Engineering.
• Estimate impact of Medbridge products on patient outcomes using experimental methods.
• Collaborate with product, engineering, clinical, and customer teams to deploy models.

Conhecimentos

Python

SQL

Causal inference

Experimentation

Production ML

Data storytelling

Formação académica

Statistics

Biostatistics

Computer Science

Economics

Ferramentas

Snowflake

Git

Descrição da oferta de emprego

Medbridge is seeking a Staff Data Scientist to own end-to-end predictive models, from framing with product partners to deployment and monitoring in production. You will shape modeling standards, ensure reproducibility, and collaborate with clinical, product, and engineering teams to drive measurable patient-outcome improvements.

We value strong Python/SQL skills, experience with real-world healthcare data, and a track record of deploying models in production.