Quantitative ERM Analyst: Data-Driven Risk Modeling

CVS Health

South Dakota

Hybrid

USD 44,000 - 85,000

Full time

4 days ago
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Job summary

CVS Health is seeking a highly detail-oriented risk analyst to support its Enterprise Risk Management program. You will maintain and enhance quantitative models, support regulatory deliverables, and collaborate with experts across the enterprise to translate business information into rigorous quantitative analysis.

You will use SQL, Excel, VBA, and when applicable R or Python to extract data, run analyses, and produce repeatable reporting, while ensuring clear documentation and auditable data

Qualifications

  • Foundational experience with quantitative analysis, financial or statistical modeling, or complex analytical coursework or projects.
  • Proficiency in Microsoft Excel, with working knowledge of SQL and VBA.
  • Demonstrated attention to detail and commitment to accuracy, completeness, data integrity, and clear, well-organized documentation.
  • Ability and willingness to learn complex quantitative models, understand how their components connect, and build the subject-matter expertise needed to maintain and enhance them over time.
  • Self-starter mindset with the ability to ask thoughtful questions, identify issues, recommend practical improvements, incorporate feedback, and take increasing ownership of assignments.

Responsibilities

  • Develop a deep understanding of assigned quantitative risk models-including their data flows, calculations, assumptions, dependencies, controls, outputs, and business context-and take increasing ownership of their ongoing maintenance and enhancement.
  • Support the enterprise economic capital model and other risk-quantification tools by investigating issues, evaluating and implementing changes, testing and reconciling results, and explaining the rationale and impact of updates.
  • Partner with subject matter experts and cross-functional teams to collect and interpret data, understand source-system and business definitions, develop or validate assumptions, and translate business information into quantitative analysis.
  • Prepare and analyze complex datasets through profiling, cleansing, standardization, validation, reconciliation, transformation, aggregation, trend analysis, and data-quality control checks.
  • Use SQL, Excel, and, where applicable, VBA, R, or Python to extract and transform data, perform quantitative and statistical analysis, support modeling, automate processes, and produce repeatable reporting.
  • Maintain clear, accurate, and auditable documentation of data sources, business rules, model methodologies, assumptions, limitations, changes, testing, results, and governance activities within designated systems and tracking tools.
  • Support stress-scenario development and execution, enterprise risk quantification, analysis of business processes.
  • Build productive relationships with internal and external stakeholders, communicate technical concepts clearly to technical and non-technical audiences, provide timely status updates, and raise issues promptly.
  • Take ownership of assignments, ask thoughtful questions, incorporate feedback, and proactively identify opportunities to improve data quality, model controls, documentation, and process efficiency while supporting broader ERM initiatives.

Skills

Quantitative analysis
Financial/statistical modeling
Excel
SQL
VBA
Attention to detail
Documentation
Self-starter

Education

Bachelor's degree in quantitative/analytical/finance/business or related field

Tools

PowerQuery
R
Python

Job description

CVS Health is seeking a highly detail-oriented risk analyst to support its Enterprise Risk Management program. You will maintain and enhance quantitative models, support regulatory deliverables, and collaborate with experts across the enterprise to translate business information into rigorous quantitative analysis.

You will use SQL, Excel, VBA, and when applicable R or Python to extract data, run analyses, and produce repeatable reporting, while ensuring clear documentation and auditable data

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