Senior Machine Learning Scientist

Teladoc Health•Uniondale, NY

1d•$150,000 - $175,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

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 code 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.