Sr Analyst, Network Analytics

About the role

The Senior Analyst, Network Analytics supports SCAN’s strategic decision-making across Network, Medical Economics, and related business partners by delivering analytics that improve network performance, affordability, access, and provider strategy.

Responsibilities

• Develop and maintain analyses related to network performance, provider utilization, total cost of care, leakage, steerage, out-of-network utilization, and site-of-care patterns to support affordability and network strategy decisions.
• Apply advanced analytics to network business objectives, including provider segmentation, opportunity modeling, stochastic modeling for risk-sharing contracts, scenario modeling, and other quantitative methods that support network optimization and contracting strategy.
• Support provider contracting strategy through analyses of reimbursement, utilization, referral patterns, competitive positioning, contract performance, and scenario modeling, including benchmarking utilization and cost patterns against fee-for-service Medicare to identify opportunities where changes in risk relationships, reimbursement approach, or network strategy may be warranted.
• Produce actionable reporting and insights on provider, market, and network performance for business partners in Network, Medical Economics, Finance, and other stakeholder groups.
• Analyze cost and utilization drivers across providers, specialties, facilities, service categories, and markets, and identify opportunities to improve value, efficiency, and member access.
• Partner with cross-functional stakeholders to align on business priorities, structure analyses, interpret findings, and communicate clear recommendations and/or trade-offs.
• Ensure analytic rigor, documentation, and data quality in recurring and ad hoc analyses, including validation of methods, assumptions, and outputs.
• Contribute to the development of analytic tools, datasets, dashboards, and repeatable frameworks that improve the scalability, sophistication, and consistency of network analytics.

Qualifications

• Graduate or Advanced Degree or equivalent experience in analytics, economics, statistics, mathematics, public health, health administration, finance, data science, geography/GIS, actuarial science, or a related quantitative field.
• GIS or geospatial analytics experience, including tools such as ArcGIS, QGIS, or geospatial Python/R libraries required.
• Familiarity with fee-for-service Medicare reimbursement and utilization benchmarking required.
• Experience with Medicare Advantage, Medicaid, Commercial, or value-based care analytics required.
• Experience specific to healthcare claims, provider, eligibility, reimbursement, and/or contract data in a payer or managed care environment strongly preferred.
• Experience supporting provider contracting, network strategy, network adequacy, geospatial access analysis, reimbursement analysis, or provider performance analytics strongly preferred.
• Experience using advanced analytics to solve healthcare or network problems, including statistical modeling, predictive analytics, segmentation, scenario modeling, or optimization approaches, strongly preferred.
• Actuarial experience or exposure is preferred but not required.
• Experience building agentic workflows (e.g., automated anomaly detection to identify network leakage) preferred.
• Experience working in modern data platforms such as Snowflake and/or Databricks.
• Strong SQL skills and ability to work with large, complex healthcare datasets.
• Ability to partner with data engineering teams to define the underlying data models for provider and contract entities within the data warehouse.
• Ability to perform geospatial, access, adequacy, and disruption analyses; experience in ArcGIS or similar software preferred.
• Ability to benchmark utilization and reimbursement patterns against external reference points such as fee-for-service Medicare.
• Ability to translate complex analyses into clear business insights and recommendations.
• Ability to manage multiple priorities and work effectively in a cross-functional environment.
• Strong quantitative skills, including statistical, predictive, or scenario-based analytic methods.
• Proficiency in Python, R, or similar analytical tools.
• Strong data visualization and presentation skills.