Senior Machine Learning Engineer

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Monogram Health

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2019

⚕️ Healthcare Insurance

🤝 B2B

👥 B2C

Healthcare Insurance • B2B • B2C

Monogram Health is a leading multispecialty provider that delivers in-home, evidence-based care and benefit management for patients with complex, multiple chronic conditions, with a strong focus on chronic kidney disease and related metabolic, cardiovascular, pulmonary, and behavioral health needs. The company coordinates multispecialty clinical teams (nephrology, cardiology, endocrinology, pulmonology, behavioral health, and palliative care), provides 24/7 home-based support including home dialysis and medication management, and partners with payers and physician groups to improve outcomes, reduce hospitalizations, and lower costs.

📋 Description

• Own and implement end-to-end ML workflows, including model versioning, testing, containerization, automated deployment pipelines (CI/CD), and post-deployment monitoring for performance and data drift • Independently develop and deploy scalable, robust machine learning models to support Monogram’s operational and clinical objectives • Conduct data analyses of large datasets to provide insights that aid strategic planning, risk assessments, and resource allocation • Identify and bridge research and production gaps related to model outputs or explore alternative use cases for existing models to enhance reporting and decision-making processes • Partner with program owners and data scientists to understand program goals, key performance indicators, and operational details to build and ensure that machine learning models are effectively aligned with program intentions • Create and present machine learning solution proposals to program owners, aiming to secure buy-in and facilitate the implementation of data-driven solutions • Identify, promote, and adhere to best practices in software development, data engineering, and machine learning to ensure high-quality and maintainable code • Remain current with the latest advancements in machine learning and data science, applying new techniques and methodologies to improve model performance and reliability • Continuously evaluate efficiency, accuracy, maintain and update deployed models, troubleshooting any issues that arise during deployment.

🎯 Requirements

• Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field • Master’s degree preferred • Minimum of five (5) years of experience in ML Ops or DevOps • Experience scaling AI products and machine learning models • Use of Python for machine learning model development and deployment, and using SQL, Databricks & PySpark to extract and manipulate data for data engineering tasks • Experience with ML Ops tools and practices (e.g., MLflow, GitHub Actions, Docker, model registries, Azure ML) • Proficiency presenting technical concepts and models to business and executive stakeholders effectively • Proficient in developing code and analyses following good software development practices • Proficiency in packaging and deploying models in production environments, ideally using Azure cloud services • Enhanced understanding of model monitoring, data drift detection, and model retraining strategies • Experience with version control systems (GIT), CI/CD pipelines, and test-driven development • Familiarity with cloud computing platforms, preferably Azure, for deploying and managing data science solutions • Evidence of advanced problem-solving abilities and a proactive approach to identifying and addressing business challenges through data-driven solutions • Demonstrated teamwork and collaboration skills, with the ability to work effectively in cross-functional teams.

🏖️ Benefits

• Comprehensive Benefits - Medical, dental, and vision insurance, employee assistance program, employer-paid and voluntary life insurance, disability insurance, plus health and flexible spending accounts • Financial & Retirement Support – Competitive compensation, 401k with employer match, and financial wellness resources • Time Off & Leave – Paid holidays, flexible vacation time/PSSL, and paid parental leave • Wellness & Growth – Work life assistance resources, physical wellness perks, mental health support, employee referral program, and BenefitHub for employee discounts