Senior Manager, Data Engineering, Provider Analytics

HealthFirst

About The Position

Healthfirst is in need of a Senior Manager, Data Engineering, Provider Analytics who will be responsible for improving the value and utility of provider data and provider data products. This role will work to create high-impact data products in the service of our internal business and external provider partners that help improve: · health outcomes, experience, and satisfaction of our members · effectiveness and efficiency of processes and operations · growth and profitability of the enterprise This role will also work on developing provider-facing data products that share actionable insights with our provider partners (including at the point of care).

Requirements

Must have a Bachelor’s degree or foreign education equivalent in Computer Science, Electronics Engineering or related field and five (5) years of experience performing machine learning (ML) infrastructure and ML operations development within a health plan or provider organization.
Must also possess the following: Demonstrated expertise (DE) using AWS or other public cloud platforms for fulfilling Data Engineering responsibilities; DE performing data management and quality assurance; DE engineering distributed systems and data infrastructure; and DE programming using R, python, Java, and C#

Responsibilities

Improve the value and utility of provider data and provider data products.
Create high-impact data products in the service of our internal business and external provider partners that help improve health outcomes, experience, and satisfaction of our members, effectiveness and efficiency of processes and operations, and growth and profitability of the enterprise.
Collaborate internally to build and maintain provider data assets and documentation.
Manage and ensure data quality and assurance for the Provider Analytics team.
Collaborate with the Provider Analytics team and Hyphen to build ML-based and provider facing decision support tools.
Collaborate with the broader data management and data engineering teams to establish standards and practices around ML Ops, including governance, compliance, and data security.