Senior Data Scientist - Time Series Forecasting Specialist

At CVS Health, we’re building a world of health around every consumer and surrounding ourselves with dedicated colleagues who are passionate about transforming health care.

As the nation’s leading health solutions company, we reach millions of Americans through our local presence, digital channels and more than 300,000 purpose-driven colleagues – caring for people where, when and how they choose in a way that is uniquely more connected, more convenient and more compassionate. And we do it all with heart, each and every day.

Senior Data Scientist – Time-Series Forecasting Specialist

A Brief Overview

Our Consumer Analytics team provides insights, designs personalized outreaches and nudges, and experiments at impressive scale: >100M messages per year. These messages educate members on truly meaningful things like staying adherent to medications, avoiding admissions to hospitals, and improving overall health (not about getting people to click on ads!).

We do this through a combination of rules, statistical, and ML models that identify who to target, an experimentation platform that we’ve built that allows complex journeys and test designs, and a combination of channels and vendors that deliver messages including SFMC. We do this hand in hand with our data engineering and marketing partners.

You will be part of a team that brings our insights and personalized outreaches and nudges to the B2B clients and their members (think health plans or employers providing health insurance and services to their employees).

As a member of our team, you will focus on improving our forecasting toolkit, allowing us to provide forward-looking insights to our clients, set performance guarantees, and focus on members with the greatest expected needs more proactively. Experience in analyzing and forecasting time series is key for this role!

What you will do:

• Lead the development and standardization of forecasting applications using time series techniques in the service of providing insights to health plans, setting performance guarantees with clients, and driving improved medication adherence with members.
• Write complex and efficient code in SQL, Python or R and leverage Exploratory Data Analysis techniques to develop insights from multiple data sources in a cloud environment.
• Design, execute, and analyze experiments (e.g., A/B testing, controlled trials) to validate model impact and continuously improve forecasting accuracy and business outcomes.
• Collaborate with a multi-disciplinary team of data scientists and engineers that develops and deploys robust machine learning and statistical models focused on time series forecasting, causal inference, and experimentation to optimize patient engagement tactics, medication adherence, and patient experience.
• Consults with internal clients to identify opportunities to implement data science solutions to business problems at an advanced level.
• Effectively collaborate with Data Engineering, IT and other technical teams to onboard new data sources, create feature stores and optimize/ automate model development and deployment processes (Github, MLOps etc.).
• Collaborate effectively with business, marketing, and other stakeholders across the organization.
• Present recommendations to senior staff and internal clients.
• Rapidly iterate through solutions to figure out what works best.
   

The ideal candidate:

• Has substantial experience applying time series forecasting methods in real-world settings, including model selection, validation, evaluation, and deployment.
• Has an in-depth understanding of time series models, including classics like SARIMA, exponential smoothing, time series regressions and machine learning methods like LSTM, XGBoost, etc.
• Has a solid grasp of foundations and experience with causal inference methods (e.g., difference-in-differences, synthetic control, instrumental variables) and experimental design.
• Wants to do something that is innovative from a data science perspective and provides tangible real-world impact
• Is okay with ambiguity and can creatively build solutions that solve problems, even if those problems are not expressed in formal ways like BRDs
• Has experience working as a social scientist and as a data scientist
• Loves innovating and working on small teams that get stuff done and have a lot of autonomy
• Healthcare background is a plus