Job Description

Your Role

The Product Strategy Insights & Analytics team partners with the Product Strategy and Marketing organization to deliver analytical support and financial modeling for product development at Blue Shield of California. This team and position play a key role in equipping strategists with data-driven insights that help shape initiatives aimed at making healthcare more affordable. By applying healthcare cost analysis, forecasting, and increasingly sophisticated predictive modeling and machine learning techniques, the team provides a deep understanding of cost drivers, trends, utilization patterns, and risk factors to support strategic decision‑making. The Product Insights and Reporting, Principal will report to the Senior Manager of Insights.

Our leadership model is about developing great leaders at all levels and creating opportunities for our people to grow – personally, professionally, and financially. We are looking for leaders that are energized by creative and critical thinking, building and sustaining high-performing teams, getting results the right way, and fostering continuous learning.

Your Work

In this role, you will:

• Leverage advanced analytics, predictive modeling, and machine learning techniques to collect, evaluate, and transform complex datasets into financial models, scenario analyses, and cost‑of‑care forecasts that estimate potential savings and inform cost/benefit evaluations of strategic options
• Integrate and reconcile data from multiple sources, applying statistical and ML‑based methods to produce unified reporting across cost, quality, utilization, and risk metrics
• Participate in internal workgroups and stakeholder meetings to provide analytical insight and support for the implementation and enhancement of new programs, products, and services
• Collaborate with key operational leaders to establish credible assumptions to support modeling of proposed initiatives to mitigate cost of healthcare trend while maintaining/improving the quality of care
• Analyze data to identify opportunities to improve healthcare quality, reduce costs and enhance member outcomes
• Present complex insights to executive leadership and key stakeholders in a clear, actionable manner
• Design, build, validate, and maintain predictive models and machine learning solutions that guide product development, product enhancement, and strategic decision‑making

Your Knowledge and Experience

• Requires a bachelor's degree or equivalent experience; advanced degree (e.g., MA, MBA) in healthcare economics, actuarial science, or related field preferred
• Requires at least 10 years of prior relevant experience, including at least 3 years of experience with Predictive Modeling and Machine Learning
• Requires understanding of statistical methods and advanced modeling techniques (e.g., SVM, K-Means, Random Forest, Boosting, Bayesian inference, natural language processing)
• Requires strong analytic and independent problem-solving skills with proficiency in data analysis tools
• Requires high-level proficiency in the following technical skills:
  • Highly proficient in scalable data transformation techniques, using SQL Programming – preferably with Netezza, Oracle
  • Tableau Reporting
  • Strong expertise in applied statistics, data mining, or other quantitative discipline using Python required. SAS Programming (Advanced Programmer Credential or Certification) strongly preferred; other statistical modeling tools such as R Programming are also acceptable
• Proficient in experimentation design and A/B testing
• Advanced knowledge and experience in healthcare (managed care, academic, or gov't payer), healthcare data and managed health plan operations strongly preferred
• Excellent communication and presentation skills with the ability to convey complex information to executive leadership and other stakeholders
• Ability to partner, collaborate with, and lead relevant stakeholders across diverse functions and experience levels

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Hybrid

This role requires employees to be in-office based on our hybrid workplace model, balancing purposeful in-person collaboration with flexibility. For most teams, this means coming into the office two days each week.

Employees living more than 50 miles from an office location will work with their manager to determine in-office time based on business need.