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.

Qualifications Expected for Position

• 8+ years of experience as a Machine Learning Scientist, Data Scientist or in a similar role within SaaS or consumer technology companies.
• A Master's degree or higher in computer science, operations research, machine learning, information systems, engineering, or a related field
• Demonstrated depth of experience developing clean, robust, and reusable production-quality code using Python, Spark, and SQL.
• Extensive experience designing, building and operating production machine learning systems, including scalable software, distributed data processing, reusable feature engineering pipelines, model deployment, monitoring and continuous improvement.
• Strong understanding of statistical modeling, machine learning algorithms, experimentation, model evaluation, forecasting, and explainability techniques, with the ability to select appropriate approaches based on business and technical constraints.
• Excellent data analysis skills and bias to deliver, measure and iterate using experimentation and statistical analysis
• Strong system design skills with the ability to architect scalable, maintainable, and observable machine learning solutions.
• Ability to translate machine learning solutions into measurable business outcomes and effectively communicate technical decisions, tradeoffs, and expected value to both technical and business stakeholders.

Bonus Qualifications

• Hands-on experience with modern data and ML platforms such as Databricks, MLflow, Delta Lake, Airflow, Terraform, or equivalent cloud-native technologies.
• Experience building AI-powered applications using Large Language Models (LLMs), embeddings, vector databases, retrieval-augmented generation (RAG), agentic workflows, or equivalent AI technologies is highly desirable.
• Experience applying machine learning, forecasting, optimization, or decision science techniques to large-scale operational, logistics, marketplace, or network optimization problems.
• Experience working with healthcare data (e.g., claims or EHR) is a plus.
• Great active listening skills to infer product/business needs and underlying context.
• Ability to collaborate effectively with peers, and respect for member privacy.

The base salary range for this position is $150,000 - $175,000. In addition to a base salary, this position is eligible for a performance bonus and benefits (subject to eligibility requirements) listed here: Teladoc Health Benefits 2026. Total compensation is based on several factors including, but not limited to, type of position, location, education level, work experience, and certifications. This information is applicable for all full-time positions.

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We follow a Flexible Vacation Policy, intended for rest, relaxation, and personal time. All time off must be approved by your manager prior to use. You will also receive 80 hours of Paid Sick, Safe, and Caregiver Leave annually. This applies to full-time positions only. If you are applying for a part-time role, your recruiter can provide additional details.

As part of our hiring process, we verify identity and credentials, conduct interviews (live or video), and screen for fraud or misrepresentation. Applicants who falsify information will be disqualified.

Teladoc Health will not sponsor or transfer employment work visas for this position. Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future.