Staff 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.

Position summary

Extracts knowledge and insights from data in order to investigate complex business problems through a range of data preparation, modeling, analysis and/or visualization techniques, including predictive analysis, business intelligence, pattern recognition, operational effectiveness and/or economic forecasting. Key skills include data analysis techniques (e.g., A/B testing, as‐ sociation rule learning, cluster analysis, pattern recognition and predictive modeling), data analysis and visualization software (e.g., Domo, Looker, PowerBI, Qlik, Tableau, SiSense), objectoriented programming languages (Java, Python, R, Ruby, Scala, C++) and/or data engineering skills (relational or NoSQL databases, ETL tools).

What you will do

• Drives the development and implementation of advanced machine learning mod‐ els and algorithms to solve complex healthcare problems, leveraging techniques such as predictive modeling, deep learning, and natural language processing.

• Collaborates with multiple departments, including data scientists, clinicians, and Information Technology (IT) professionals, to understand business requirements, define machine learning projects, and prioritize initiatives based on strategic objectives.

• Interfaces with stakeholders to define performance metrics and evaluation methodologies for machine learning models, contributing to rigorous testing, validation, and performance monitoring of models to ensure accuracy and reliability.

• Designs and implements scalable and efficient machine learning systems, includ‐ ing data pipelines, preprocessing, feature engineering, and model training, ensuring the quality and integrity of healthcare data used for analysis.

• Advises on the optimization and improvement of data pipelines, model training processes, and infrastructure to enhance efficiency, scalability, and performance of machine learning solutions.

• Consults on and presents technical findings, insights, and recommendations to both technical and non-technical stakeholders, contributing to the dissemination and application of machine learning insights in the healthcare industry.

• Ensures compliance with data privacy regulations, ethical guidelines, and industry standards in machine learning engineering, supporting the development of proto‐ cols and practices for model interpretability, fairness, and transparency.

• Manages team performance through regular, timely feedback as well as the formal performance review process to ensure delivery of exceptional services and en‐ gagement, motivation, and team development.

• Stays up-to-date with the latest advancements in machine learning and related technologies, continuously exploring and evaluating new algorithms and method‐ ologies to enhance machine learning capabilities in healthcare applications.

Required Qualifications

• 7+ years of coding experience using Python with expertise in solution and software development.

• 7+ years in DevOps/MLOps and a deep understanding of software engineering principles.

• 7+ years of experience in TensorFlow and PyTorch, with additional cloud platform familiarity.

Preferred Qualifications

• Proven ability to manage the production lifecycle of an MLOps pipeline.

• Deep understanding of model deployment and serving.

• Experience with orchestration and containerization methodologies.

• Familiarity with model serving tools such as MLflow, Databricks

• Serving SageMaker etc (TensorFlow Serving is a plus)

• Experience implementing CI/CD pipelines for ML projects.

• Competence in developing automation pipelines (e.g., using GitHub Actions).

• Proficiency in Infrastructure as Code tools (e.g., Terraform, Databricks Asset Bundles,etc).

• Strong understanding of monitoring and observability, including logging tools (e.g., Prometheus, Grafana) and drift detection.

Education

Bachelor degree from accredited university or equivalent work experience (HS diploma + 4 years relevant experience)

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