About the role
Join
the team leading the next evolution of virtual care. At Teladoc Health, you are empowered to bring your true self to work while helping millions of people live their healthiest lives. Here you will be part of a high-performance culture where colleagues embrace challenges, drive transformative solutions, and create opportunities for growth. Together, we’re transforming how better health happens. Summary of Position The Machine Learning effort is part of the Data Science team at Teladoc Health. In this role, you will partner with Product, Engineering, Clinical, Operations, Marketing and Data Engineering to design, build, deploy, and operate scalable machine learning and AI systems that power business-critical decision making. You will own the end-to-end machine learning lifecycle: from data and feature engineering through deployment, monitoring, experimentation, and continuous improvement (across both batch and real-time production environments). Your efforts and contributions will have a big impact on improving member and provider experience on the Teladoc Health platform. This is an opportunity to apply technical rigor, scalable data processing tools, and machine learning algorithms to solve real-world business problems while engineering, deploying, measuring, and iterating machine learning solutions in production. Essential Duties and
Responsibilities Build production ready time series models to predict real time KPIs as well as build optimal decision actions to manage the provider network for clinical operations business optimization Propose, evaluate and interpret the results of your work for clinical, product and business decision-makers and own outcomes Collaborate closely with peers and stakeholders to discover and distill
requirements of problem definitions, product features and architecture to improve clinical outcomes using insights and models Develop modular, well-tested, production-quality software using Python, Spark and SQL to build scalable data engineering, feature engineering, machine learning and AI pipelines following software engineering best practices. Design, develop, deploy and operate scalable production machine learning and AI systems, including data transformation pipelines, feature pipelines, model training, evaluation, deployment, monitoring, retraining, and experiment tracking. Ensure robust model lifecycle management through model versioning, MLflow, automated testing, CI/CD, and production monitoring. Build and optimize scalable Spark and Databricks workloads, leveraging distributed computing best practices for large-scale data processing and real-time inference. Design, evaluate and integrate Large Language Models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and other AI capabilities where appropriate to solve business problems. Monitor production models and data pipelines for data quality, feature drift, concept drift, latency, reliability, and business performance, proactively identifying and resolving issues.