Sr Machine Learning Engineer

CVS Health•Woonsocket, RI

3d•$130,832 - $203,940•Remote

About The Position

CVS Shared Services Resources LLC, a CVS Health company, is hiring for the following role in Woonsocket, RI: Sr Machine Learning Engineer to design and implement ML solutions that automate pharmacy operations and improve patient care outcomes. Partner directly with retail pharmacy stakeholders to identify automation opportunities and define ML solution requirements. Conduct feasibility analysis using Snowflake data warehouse to assess data availability and quality for proposed ML initiatives. Present technical findings and recommendations to business teams, translating complex ML concepts into actionable insights. Lead requirements gathering sessions with Lead Data Scientist to scope pharmacy-specific ML projects. Design and develop ML models for pharmacy growth and operations to drive incremental scripts and operational efficiencies. Build and optimize feature engineering pipelines for pharmacy-specific use cases. Implement diverse ML approaches including deep learning (LSTM, GRU, BERT, Transformers), gradient boosting (XGBoost, LightGBM), neural networks, ensemble methods, anomaly detection algorithms, and reinforcement learning for sequential decision-making. Leverage AutoML platforms for rapid prototyping and baseline model development. Conduct model evaluation and selection through systematic experimentation in Jupyter notebooks. Develop and maintain Kubeflow pipelines for model training and deployment workflows. Deploy models to development and testing environments on Azure cloud infrastructure. Support potential migration from Azure to GCP and adoption of Model Garden framework. Collaborate with Data Engineering team on infrastructure requirements and data pipeline dependencies. Document technical specifications and model performance metrics. Ensure compliance with healthcare data regulations and pharmacy safety standards. Telecommuting available. Multiple Positions.

Requirements

Master’s degree (or foreign equivalent) in Computer Science, Computer Engineering, Information Technology, Engineering, or a related field and two (2) years of experience in the job offered or related occupation.
Two (2) years of experience in CI/CD, Jenkins, GIT, and DevOps.
Two (2) years of experience in JavaScript.
Two (2) years of experience in Python, and Node.js.
Two (2) years of experience in Docker or Kubernetes.
Two (2) years of experience in R, Spark, and PyData ecosystem.
Two (2) years of experience in Domain support for healthcare or retail organization.
Two (2) years of experience in Developing backend services, performing code reviews, and collaborating with peers on software development solutions.
Two (2) years of experience in Feature engineering, model training, hyperparameter tuning, distributed model training, and supervised and unsupervised learning implementation.
Two (2) years of experience in Quantitative analysis techniques, including clustering, regression, and pattern recognition.
Two (2) years of experience in Designing data architectures, including data pipelines, distributed computing engines, and machine learning infrastructure design.
Two (2) years of experience in Developing and deploying predictive models or ML systems in a cloud environment (GCP, AWS, or Azure).

Responsibilities

Design and implement ML solutions that automate pharmacy operations and improve patient care outcomes.
Partner directly with retail pharmacy stakeholders to identify automation opportunities and define ML solution requirements.
Conduct feasibility analysis using Snowflake data warehouse to assess data availability and quality for proposed ML initiatives.
Present technical findings and recommendations to business teams, translating complex ML concepts into actionable insights.
Lead requirements gathering sessions with Lead Data Scientist to scope pharmacy-specific ML projects.
Design and develop ML models for pharmacy growth and operations to drive incremental scripts and operational efficiencies.
Build and optimize feature engineering pipelines for pharmacy-specific use cases.
Implement diverse ML approaches including deep learning (LSTM, GRU, BERT, Transformers), gradient boosting (XGBoost, LightGBM), neural networks, ensemble methods, anomaly detection algorithms, and reinforcement learning for sequential decision-making.
Leverage AutoML platforms for rapid prototyping and baseline model development.
Conduct model evaluation and selection through systematic experimentation in Jupyter notebooks.
Develop and maintain Kubeflow pipelines for model training and deployment workflows.
Deploy models to development and testing environments on Azure cloud infrastructure.
Support potential migration from Azure to GCP and adoption of Model Garden framework.
Collaborate with Data Engineering team on infrastructure requirements and data pipeline dependencies.
Document technical specifications and model performance metrics.
Ensure compliance with healthcare data regulations and pharmacy safety standards.