Overview
Designs and implements systems used to develop, test, and deploy machine learning models that support business processes and improve business outcomes. Leverages technologies and platforms to support reproducible feature engineering and optimized machine learning model deployment at scale. Leverages continuous integration and continuous delivery of best practices, including test automation and monitoring, to ensure successful deployment of machine learning models. Works under general supervision.
Compensation Range:$122,300.00 - $164,000.00 Annual
What We Provide
- Referral bonus opportunities
- Generous paid time off (PTO), starting at 20 days of paid time off and 9 company holidays
- Health insurance plan for you and your loved ones, Medical, Dental, Vision, Life and Disability
- Employer-matched retirement saving funds
- Personal and financial wellness programs
- Pre-tax flexible spending accounts (FSAs) for healthcare and dependent care
- Generous tuition reimbursement for qualifying degrees
- Opportunities for professional growth and career advancement
- Internal mobility, generous tuition reimbursement, CEU credits, and advancement opportunities
- Interdisciplinary network of colleagues through the VNS Health Social Services Community of Professionals.
What You Will Do
- Designs and implements scalable and reliable machine learning (ML) systems used to develop, test, and deploy machine learning models.
- Leverages technologies and platforms to support reproducible feature engineering and optimized machine learning model deployment at scale.
- Leverages continuous integration/continuous delivery (CI/CD) best practices, including test automation and monitoring, to ensure successful deployment of machine learning models.
- Collaborates with data scientists on designing a continuous model retraining process that can integrate with automated model redeployment processes.
- Creates robust monitoring solutions to understand model performance and manage model life cycles via a centralized model registry.
- Expands and optimizes data pipeline architecture in order to support building of machine learning platforms from a wide variety of complex data sets.
- Partners with data scientists and IT data engineers to understand business priorities, frame machine learning problems, and architect machine learning solutions.
- Identifies gaps and evaluates relevant tools and cloud computing technologies as needed to improve machine learning processes and build effective solutions.
- Works with clinical operations, business owners, and/or IT to understand how VNS Health applications commit records into database systems.
- Ensures data quality throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, and transformation.
- Guides members of the Data Science team in code reviews, documentation and software engineering life cycle.
- Participates in special projects and performs other duties as assigned.
Qualifications
Education:
- Bachelor's Degree in Computer Science or a related discipline required
- Master's Degree in Computer Science or a related discipline preferred
Work Experience:
- Minimum of four years of experience deploying and productionizing machine learning models required
- Experience with data pipeline and workflow management tools (e.g. Airflow) required
- Programming expertise in Python or R required
- Experience with designing, configuring, and using ML engineering platforms (e.g. Sagemaker, MLflow, Kubeflow) required
- Experience with building, deploying, and monitoring ML pipelines required
- Experience with building or using ML Ops framework required
- Experience in container services, Kubernetes and Docker required
- Experience with cloud computing (e.g. AWS) and columnar databases (e.g. Snowflake) in a cloud environment required
- Experience with version control, especially Git/GitHub required
- Effective oral, written and interpersonal communication skills required
- Experience with Unix administration preferred
- Experience building and deploying machine learning algorithms in a health care setting preferred
- Experience with medical claims, electronic medical records, and clinical assessment data preferred