Data Scientist - Clinical Risk Modeling

CVS Health • Annapolis, MD • 20h ago

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.

Company: Oak Street Health

Title: Data Scientist

Company Description

Oak Street Health is a rapidly growing company of primary care centers for adults on Medicare in medically-underserved communities where there is little to no quality healthcare. Oak Street's care is based on an entirely new model that is based on value for its patients, not on volume of services. The company is accountable for its patients' health, spending more than twice as long with its patients and taking on the risks and costs of their care. For more information, visit http://www.oakstreethealth.com .

Role Description

Oak Street Health is looking for an data scientist to join our care and risk modeling team. This team is responsible for delivering high quality modern data solutions through collaboration with engineers, analysts, and product teams in a fast-paced, agile environment leveraging cutting-edge technology to reimagine how healthcare is provided. In this position, you will be responsible for taking on new initiatives associated with enhancements, expansion, maintenance and scaling in data science. As a member of our team, you will make an immediate impact as you help expand our data science capabilities across our whole care model. This is a fast-paced role with high growth, visibility, impact, and where many of the decisions for new projects will be driven by you from inception through production.

Core Responsibilities:

Leverage Oak Street's best-in-class data warehouse to build new predictive models focused on improving the care model and patient outcomes.

Achieve best in class model accuracy through a collaborative approach to feature engineering.

Explore, validate, interpret, and summarize large amounts of data with various @types/structures using advanced statistical and mathematical approaches.

Build dashboards and data visualizations to effectively communicate analysis results and support decision making processes with stakeholders.

Collaborate closely with clinical and business owners to integrate predictive applications across the organization.

Other duties as assigned.

Required Qualifications

Bachelor's degree and 1+ years of relevant analytics experience.

Experience using SQL and Python in a cloud (GCP, Azure, AWS) to solve analytical problems.

Experience solving analytics problems using advanced statistics, machine learning, and mathematical algorithms.

Preferred Qualifications

Experience applying data science techniques such as predictive modeling, clustering, forecasting, etc. to solve organizational healthcare tasks.

Excellent command of data science libraries in Python or R.

Strong written and verbal communication skills, including the ability to present complex ideas to various audiences.

Excellent problem-solving and analytical skills.

An ability to independently pursue and implement solutions to complex problems.

Outstanding attention to detail.

Someone who embodies being "Oaky"

What does being "Oaky" look like?

Radiating positive energy

Assuming good intentions

Creating an unmatched patient experience

Driving clinical excellence

Taking ownership and delivering results

Being scrappy

Anticipated Weekly Hours

Time Type

Full time

Pay Range

The typical pay range for this role is:

$64,890.00 - $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: 10/30/2025

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.

We are an equal opportunity and affirmative action employer. We do not discriminate in recruiting, hiring, promotion, or any other personnel action based on race, ethnicity, color, national origin, sex/gender, sexual orientation, gender identity or expression, religion, age, disability, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.

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