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We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. The Consumer Engagement & Analytics team is helping lead the effort to drive improved consumer experience by partnering with key business areas across CVS Health to deliver impactful analytic products and insights. As a member of the Pricing Analytics team, you will be at the forefront of delivering high visibility products for key CVS Retail strategic priorities. Specifically, you will have the opportunity to drive traffic, revenue and customer satisfaction in CVS Health’s Front Store business using data science to optimize pricing decisions.
Job Responsibility:
Solve complex problems, translate business requirements into testable hypotheses and measurable metrics, build causal, causal ML, and/or predictive models to understand localized trends, assess the competitive environment, and measure short- and long- term customer impacts to optimize pricing decisions
Develop and leverage in-depth knowledge of Retail Merchandising business processes, translate key drivers of business value to analytics opportunities, and simulate impact of business decisions to improve customer experience
Guide the technical approach, leverage large datasets, and build scalable and deployment ready models that work well with CVS Retail’s diverse product portfolio, develop robust methodologies and metrics to assess model performance
Stay up to date on the state-of-the-art modeling methodologies and latest research
Utilize the latest developments in the generative models (LLMs and other AI models) to improve and enrich the performance of the pricing models, rethink the deployment readiness and scalability of the models, and automation opportunities using agents
Work cross functionally with a range of technical and non-technical stakeholders including business partners, engineers, and other data and analytics teams. Share progress, findings, recommendations, and their business implications. Present meaningful and insights-driven materials on analytical results with recommendations and go forward planning to guide a variety of audiences including internal stakeholders and senior leadership
Requirements:
Bachelor’s degree in a quantitative field such as economics, statistics, or a related field. Master’s degree is preferred
3+ years of work experience in retail, consulting, or a related field
Demonstrated experience applying statistical models and/or causal inference techniques (such as Hierarchical GLM, Double ML, synthetic control, and other quasi experimental techniques). Familiarity with surrogate models
Strong Python, R, and SQL skills, proficiency in working with large datasets
Experience working with a data engineering/MLOps team to productionize data science models, familiarity with version control (GitLab or GitHub), and ML platforms (AWS SageMaker, Databricks, GCP Vertex AI, etc.)
Excellent communication and presentation skills and attention to detail. Worked in an agile environment, has the flexibility to adapt to changing business needs
Nice to have:
PhD in economics or equivalent
5+ years of work experience in retail, consulting, or a related field
Experience developing elasticity models, in depth understanding of merchandising (price, promotion, assortment) concepts and metrics in retail
Experience with managing large scale projects and working with multiple business stakeholders
What we offer:
Affordable medical plan options, a 401(k) plan (including matching company contributions), and an employee stock purchase plan
No-cost programs for all colleagues including wellness screenings, tobacco cessation and weight management programs, confidential counseling and financial coaching
Benefit solutions that address the different needs and preferences of our colleagues including paid time off, flexible work schedules, family leave, dependent care resources, colleague assistance programs, tuition assistance, retiree medical access and many other benefits depending on eligibility