Senior Manager, Data Science

Job Summary

The Senior Manager Data Science

Key Responsibilities:

• Provide cross-functional technical leadership, coaching and guidance to leaders across the software development organization.
• Maximize the capability and capacity of resources to deliver high quality solutions on time and within scope.
• Accountable for establishing and managing financial budgets
• Run the business based on operational metrics and status. This includes but is not limited to queue churn, trending, resource utilization, risk assessment and mitigation, project initiatives, and department related data elements
• Make timely and effective decisions to ensure the team is prepared to deliver solutions on a strategic roadmap not just for the short-term
• Lead cross-functional change initiatives and develop strategies to support change in engineering processes and strategy
• Accountable for ensuring the capability and capacity of the team to advance and leverage the latest in programming languages, industry standards and software development methodologies. • Become a trusted advisor for executive leaders across the company.
• This position may require availability outside of standard business hours as part of a rotational on-call schedule

Required Skills:

• At least 10+ years of professional experience in data science, machine learning, or applied analytics roles.
• At least 5 years of large, complex project management experience
• At least 4 years of leading a team of technical staff
• At least 4 years of experience with full lifecycle development and using Agile methods
• Deep understanding of statistical modeling, machine learning algorithms, and real-world data applications.
• Hands-on experience with data science frameworks and cloud platforms (AWS, Azure, GCP).

Preferred Skills:

• Experience designing, building and operating secure, scalable and highly available web and mobile applications, large-scale distributed systems using service-oriented architecture and message queue architecture in the cloud

• Understanding of cloud-based SaaS architectures – involves designing elastic solutions, distributed state, availability, scalability, etc.

• Adept in Object Oriented Design

• Experience in healthcare, life sciences, or human services domains.

• Familiarity with data governance, privacy, HIPAA, or FHIR standards.

• Experience in building ML platforms or MLOps workflows.

• Strong business acumen with the ability to link data insights to actionable outcomes.