Senior Data Science Manager, Spring Health

• The ideal candidate will have a strong technical background, excellent leadership skills, and a passion for driving data-driven impact
• Software engineering skills: Proficiency in both Python and R and experience deploying both in production. Familiarity with analytics engineering tools like dbt and ML platforms such as AWS Sagemaker is preferred but not required
• Statistical knowledge: Strong analytical and problem-solving skills, with a deep understanding of statistical modeling and machine learning techniques
• Machine learning expertise: 5+ years of experience iteratively building machine learning models, evaluating their performance, and deploying them in production
• Proven Leadership: 3+ years of experience in building and leading machine learning, data science, or AI teams, with a track record of delivering successful AI-driven solutions
• Management Skills: Strong leadership and management experience, with the ability to manage, coach, and develop high-performing teams
• Collaboration: Demonstrated ability to collaborate across technical and non-technical teams, fostering partnerships to deliver AI solutions that solve real-world problems
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field

Desirable

• We are seeking a Senior Data Science Manager to report to the Director, AI/ML Engineering and lead a dynamic team of data scientists
• This role involves building and deploying sophisticated algorithms that forecast cost and measure ROI, as well as developing tools that empower our internal teams to evolve our pricing strategy
• Lead efforts to design, develop, and deploy machine learning models and algorithms in production
• Collaborate with your product partner to understand business needs and translate them into AI/ML solutions
• Manage and mentor a team of data scientists, fostering a culture of collaboration, innovation, and continuous learning
• Take ownership of technical feasibility and quality by establishing and championing strong development norms and playing a hands-on role in execution
• Recruit and make hiring decisions for open positions on your team
• Collaborate with product managers, machine learning engineers, software engineers, and business stakeholders to ensure the successful integration of AI/ML technologies into products and services
• What success looks like in this role:
• Launch high impact data products that both delight customers and drive business operations
• Maintain and enhance strong engineering practices that make data products scalable, maintainable, and adaptable to evolving business needs
• Track data product success, both in traditional evaluation metrics and impact on business outcomes, using data to drive continuous improvement
• Elevate the standards of technical excellence and delivery while fostering an environment that values autonomy and purpose
• Implement feedback loops to refine ML models and ensure they meet or exceed business expectations
• High accountability and clear communication of timelines to the business, with escalation and mitigation strategies when those timelines are at-risk
• Close collaboration with your product partner(s) to understand business objectives, market demands, and the product strategy that guides our development
• Mentor and grow the talent of your team, ensuring they have the resources and guidance to excel in their roles