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. Position Summary 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. In this role, you will have the opportunity to: - Solve complex problems, translate business requirements into testable hypotheses and measurable metrics, and build causal inference, econometric, and predictive models to understand localized trends, assess competitive environments, and measure short- and long-term customer impacts to optimize pricing and promotion decisions. - Develop and leverage in-depth knowledge of Retail Merchandising and Pricing processes, translate key drivers of business value into analytics opportunities, and simulate the impact of pricing and promotional decisions to improve customer experience. - Work with large datasets, optimize code, and build scalable, deployment-ready models that work well with CVS Retail’s diverse product portfolio. - Develop robust methodologies and metrics to assess model performance and ensure statistical validity. - Stay up to date on state-of-the-art modeling methodologies, econometric techniques, and latest research in pricing and promotional analytics. - Utilize the latest developments in generative AI (LLMs and other models) to enhance pricing models, rethink deployment readiness and scalability, and explore automation opportunities using AI agents. - Collaborate cross-functionally with technical and non-technical stakeholders, including business partners, engineers, and analytics teams. Present insights-driven materials with recommendations and go-forward plans to guide internal stakeholders and senior leadership. Required Qualifications - 1+ years of work experience in retail, consulting, or a related field. - Strong programming skills in Python and SQL. - Proficiency in working with large datasets and distributed computing frameworks. - Hands-on experience with version control (GitLab/GitHub), ML platforms (AWS SageMaker, Databricks, GCP Vertex AI), and familiarity with CI/CD pipelines and MLOps tools (Kubeflow). - Experience with econometric modeling techniques (e.g., price elasticity, promotional lift, cross-effects, hierarchical models). - Experience working with state-of-the-art Deep Learning and NLP models in a non-academic setting. - Excellent communication and presentation skills, attention to detail, and ability to work with multiple stakeholders. Preferred Qualifications - 3+ years of work experience in retail, consulting, or a related field. - Strong background in pricing and promotion analytics, including elasticity modeling, cannibalization analysis, and competitive price simulation. - Experience with causal inference methods (e.g., difference-in-differences, synthetic controls, propensity score matching). - Familiarity with Bayesian hierarchical modeling and advanced time-series forecasting for retail demand. - Experience managing large-scale projects and working with multiple business stakeholders. - Designed and implemented end-to-end ML solutions, including monitoring and improving model performance in production, experiment tracking, and setting up data drift monitoring systems. Education - Bachelor’s degree in quantitative fields such as Computer Science, Statistics, Economics, Engineering, or Data Science. - Master’s degree in Econometrics, Applied Statistics, or Data Science preferred. Anticipated Weekly Hours 40 Time Type Full time Pay Range The typical pay range for this role is: $86,520.00 - $173,040.00 This pay range represents the base hourly rate or base annual full-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above. Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong. Great benefits for great people We take pride in our comprehensive and competitive mix of pay and benefits – investing in the physical, emotional and financial wellness of our colleagues and their families to help them be the healthiest they can be. In addition to our competitive wages, our great benefits include: - 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. For more information, visit https://jobs.cvshealth.com/us/en/benefits We anticipate the application window for this opening will close on: 04/26/2026 Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.