Data Science Manager

Ascension•Remote,

1d•$138,258 - $195,188•Remote

About The Position

Ascension is seeking a Data Science Manager to lead and mentor a specialized team of data scientists in building advanced AI solutions that directly improve patient safety and clinical quality. This role involves overseeing the technical development of predictive models and Generative AI products, ensuring they are fair, unbiased, and clinically useful. The manager will bridge the gap between data science and clinical care by translating technical breakthroughs into actionable insights for leaders and stakeholders. A key responsibility is partnering with MLOps to streamline the transition of AI models from development into production and real-world clinical use, ensuring their reliability and security.

Requirements

High School diploma equivalency with 3 years of cumulative experience OR Associate's degree/Bachelor's degree with 2 years of cumulative experience OR 7 years of applicable cumulative job specific experience required.
3 years of leadership or management experience preferred.

Nice To Haves

Master’s degree or PhD in a STEM field (such as Data Science or Health Informatics)
Deep understanding of how to navigate and interpret complex, real-world healthcare data.
At least five years of industry experience, including direct experience managing or leading teams within a professional data science environment.
Significant conceptual knowledge of modern AI frameworks and cloud platforms (specifically Google Cloud and Vertex AI) to effectively evaluate team code and strategy.

Responsibilities

Lead and mentor a specialized team of data scientists to build advanced AI solutions that directly improve patient safety and clinical quality.
Oversee the technical development of predictive models and Generative AI products, ensuring every tool developed is fair, unbiased, and clinically useful.
Bridge the gap between complex data science and bedside care by translating technical breakthroughs into clear, actionable insights for clinical leaders and stakeholders.
Partner with MLOps to streamline the transition of AI models from development into production and real-world clinical use, ensuring they remain reliable and secure over time.