Machine Learning Engineer

Machine Learning Engineer

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. Oak Street Health, part of the CVS Health umbrella, 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.

Role Description

CVS Health is looking for a machine learning engineer to support our extensive production modeling efforts. Oak Street Health has built world-class risk stratification tools to empower optimal clinical outcomes and you will have the opportunity to contribute to its continued growth through writing production-quality code. You will design, develop, test and expand Oak Street Health's internal tools that enable our Data Science teams to build, deploy and monitor models more effectively and efficiently. You will leverage both your programming and problem-solving skills to improve our model building and hosting pipelines.

Responsibilities

• Design, develop, and implement machine learning pipelines in collaboration with Data Science teams.
• Maintain and enhance best practices through code development and testing, ML model validation, and automation of tests and deployments.
• Collaborate with Data Science, Product, Engineering, and Business partners to deploy effective and efficient state-of-the-art machine learning models that address operational needs.
• Support the full MLOps lifecycle, including external data integration, data validation, model scaling, and prediction monitoring.
• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver ML models at scale.
• Utilize continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML practices adhere to Responsible and Explainable AI guidelines.
• Independently pursue and implement solutions to complex problems.

What We're Looking For

We're looking for motivated, entrepreneurial individuals with:

• Basic Qualifications
• 1+ years of experience building, scaling, and optimizing machine learning systems.
• 1+ years of experience programming with one or more modern languages, such as Python, C++, Java, etc.
• 1+ years of experience with common ML libraries (i.e. Scikit-learn, XGBoost, NumPy, Keras, PyTorch, etc.) and ML algorithms (i.e. clustering, decision trees, boosting, etc.)
• A degree in Computer Science, Mathematics, Statistics, or another quantitative discipline OR 3+ years of direct experience.
• Strong background in the foundations of machine learning and model serving.
• Proficiency with Python and ML frameworks such as Scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
• Track record of building ETL or ML model-serving pipelines.
• Ability to scale a data scientist's code into production-ready packages integrated into core OSH services.
• Strong communication and interpersonal skills with the ability to deliver complex topics to all familiarity levels.
• US work authorization.
• Growth mindset and willingness to learn.
• Someone who embodies being "Oaky".
• Preferred Qualifications
• Experience working with clinical or medical data.
• 1+ years of experience developing analytical pipelines against Cloud Data Warehouses such as BigQuery, Snowflake or Redshift using Python, SQL or PySpark.
• Experience packaging Python code into distributable & deployable packages.
• Experience with at least one major cloud platform (Azure, GCP, AWS)
• Experience with Databricks.
• Hands-on experience with NLP or generative-AI techniques

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

40

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.

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.