Senior Manager, Data Science

Senior Manager, Data Science

Key Responsibilities:

• Lead the strategy, work with various stakeholders to define the roadmap, and delivery of healthcare products
• 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

What Youll Need to Be Successful (Required Skills):

• At least 10+ years of professional experience in data science, machine learning, or applied analytics roles. Must have experience leading data science or machine learning teams.
• Experience with variable cost analysis and management for complex cloud-based solutions
• Experience in leading, coaching, and mentoring a team of data scientists; foster a culture of innovation and learning.
• Experience with full lifecycle development in product or engineering leadership using Agile methods for large, complex projects
• Deep understanding of statistical modelling, machine learning algorithms, and real-world data applications.
• Hands-on experience with data science frameworks and cloud platforms such as AWS, Azure, GCP.

Education/ Certifications:

Bachelor's degree in computer science, or related field.

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.
• Experience building ML platforms or MLOps workflows.
• Exposure to driving adoption of AI/Automation to make the product development/QA cycle better
• Experience in healthcare, life sciences, or human services domains.
• Knowledge of healthcare regulatory and compliance including data governance, privacy, HIPAA, or FHIR standards

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

We work 5 days from office.

Global Capability Centre(GCC)