Senior Data Scientist - Forecasting

CVS Health•Work At Home-Massachusetts, MA

3d•$101,970 - $203,940

About The Position

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. Build and extend the demand forecasting foundation behind CVS Retail's Unified Forecasting Platform, translating business requirements into testable hypotheses and measurable metrics, building predictive models to understand localized trends and the competitive environment, and measuring short- and long-term customer impacts for merchandising decisions. Guide the technical approach, leverage large datasets and high-performance computing techniques to build scalable, deployment ready models that work well with CVS Retail's diverse product portfolio, and develop robust methodologies and metrics to assess model performance Utilize the latest developments in generative models (LLMs and other AI models) to improve model performance and rethink the deployment readiness and scalability of those models Identify and build automation opportunities using AI agents to reduce manual effort across the broader analytics workflow 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 Work cross functionally with a range of technical and non-technical stakeholders, including business partners, engineers, and other data and analytics teams, sharing progress and findings as the work develops Present insights-driven analytical results, recommendations, and go forward planning to guide stakeholders and senior leadership Stay up to date on the state-of-the-art modeling methodologies and latest research

Requirements

3+ years of work experience as a Data Scientist in retail, consulting, or a related field
Hands on experience with building statistical, machine learning, and deep learning models
Strong Python and SQL skills, proficiency in working with large datasets
Experience writing modular, production ready code
Experience working with a data engineering/MLOps team to productionize data science models
Familiarity with version control (GitLab or GitHub)
Familiarity with cloud-based ML platforms (Azure ML, GCP Vertex AI, or similar)
Bachelor's degree in a quantitative field such as statistics, engineering, or a related field
Willingness to learn from teammates and to provide technical direction and informal mentorship to junior data scientists, while remaining hands-on with model development
Excellent communication and presentation skills, with attention to detail and ability to document technical decisions and model assumptions clearly enough for cross-functional and globally distributed teams to align without a live meeting
Worked in an agile environment, has the flexibility to adapt to changing business needs

Nice To Haves

5+ years of work experience as a Data Scientist or Senior Data Scientist in retail, consulting, or a related field
Demonstrated experience building and scaling predictive models in a production environment across a diverse, evolving product portfolio
Understanding of retail merchandising concepts such as pricing, promotion, or assortment, or experience on a team focused on supply chain, inventory, or operations analytics
M.S. or PhD in a quantitative field such as mathematics, engineering, physics, or equivalent
Portfolio of technical work samples, such as sanitized case studies, publications, conference talks, or a public git repo where permitted by a prior employer

Responsibilities

Build and extend the demand forecasting foundation behind CVS Retail's Unified Forecasting Platform.
Translate business requirements into testable hypotheses and measurable metrics.
Build predictive models to understand localized trends and the competitive environment.
Measure short- and long-term customer impacts for merchandising decisions.
Guide the technical approach, leverage large datasets and high-performance computing techniques to build scalable, deployment ready models.
Develop robust methodologies and metrics to assess model performance.
Utilize the latest developments in generative models (LLMs and other AI models) to improve model performance and rethink the deployment readiness and scalability of those models.
Identify and build automation opportunities using AI agents to reduce manual effort across the broader analytics workflow.
Develop and leverage in-depth knowledge of Retail Merchandising business processes.
Translate key drivers of business value to analytics opportunities.
Simulate impact of business decisions to improve customer experience.
Work cross functionally with a range of technical and non-technical stakeholders, including business partners, engineers, and other data and analytics teams, sharing progress and findings as the work develops.
Present insights-driven analytical results, recommendations, and go forward planning to guide stakeholders and senior leadership.
Stay up to date on the state-of-the-art modeling methodologies and latest research.