Senior AI Data Scientist

Are you seeking an exciting opportunity to join a passionate, growing, and dynamic team of professionals who support patients?

Knowtion Health is seeking a Senior AI Data Scientist who owns systems end to end — framing the problem, building and validating the models, and standing up the data pipelines and monitoring that keep them running in production.

The role is data science and engineering in equal measure: the statistical rigor to build a model that is sound, and the engineering craft to ship and operate it.

Because our models touch payments and patients, they are held to a high bar: statistically defensible, operationally reliable, and defensible to an auditor.

This is a senior individual-contributor role for someone who has both built models and kept them alive in production.

What’s Attractive to the Right Candidate?

• Knowtion Health is a growing firm in a growing industry. Our status as a leader in this industry means that we have the resources to invest in the business and to innovate.
• Our business is intensely competitive and is constantly evolving. We quickly identify new challenges and develop solutions, so you won’t simply be doing what was done last year. Our new employees are frequently pleased and surprised by how quickly we make decisions and adapt to market conditions.
• Knowtion Health culture is inviting and competitive, embracing challenge and celebrating accomplishment; dedicated colleagues striving to provide quality results that have lasting impact.

• Frame ambiguous revenue-cycle problems as tractable modeling problems and own them end to end — such as claim and invoice viability scoring, denial and underpayment prediction, revenue/ARR prediction, and work prioritization — from hypothesis to a validated, monitored result in production.
• Build and validate models with statistical rigor: thoughtful feature design, sound handling of missingness and class imbalance, calibration, honest evaluation against simple baselines, and a clear treatment of uncertainty.
• Design, build, and maintain the data pipelines feeding these models — integrating with source collections, billing, and EHR systems and internal data stores, and handling data collection, cleansing, field mapping, and payer/plan normalization — with a bias toward reliability and data quality.