Remote Staff Data Scientist, Healthcare ML & Production Models

MedBridge Inc.; Alton (IL); Remote; USD 190,000 - 220,000; Full time

Job summary

Medbridge Inc. is seeking a Staff Data Scientist to own predictive and risk-adjustment models in a remote-first, U.S.-based environment. You will collaborate with product, clinical, and engineering teams to frame problems, select methods, and ship models into production within Snowflake.

You’ll bring statistical depth, real-world healthcare data experience, and strong software engineering practices to establish modeling standards, monitoring, and responsible use of data across the product.

Qualifications

• Experience with production-grade models and deployment
• Strong statistics background with uncertainty quantification
• Clear communication with technical and clinical stakeholders
• Experience taking models from concept to shipped product features
• Ability to work with messy real-world healthcare data

Responsibilities

• Own design, development, validation, and production operation of predictive models in Snowflake
• Set methodological standards and document decisions for cross-team scrutiny
• Write tested, versioned, and reproducible code with deployment and monitoring partnerships
• Estimate impact of Medbridge products on patient outcomes using experiments and observational methods
• Collaborate with product, engineering, clinical, and customer-facing teams to put models into production and define evaluation practices

Skills

Python

SQL

Statistical reasoning

Causal inference

Communication

Education

Statistics/biostatistics/CS degree

Tools

Snowflake

Job description

Medbridge Inc. is seeking a Staff Data Scientist to own predictive and risk-adjustment models in a remote-first, U.S.-based environment. You will collaborate with product, clinical, and engineering teams to frame problems, select methods, and ship models into production within Snowflake.

You’ll bring statistical depth, real-world healthcare data experience, and strong software engineering practices to establish modeling standards, monitoring, and responsible use of data across the product.