Senior Machine Learning Scientist

Teladoc Health

Senior Machine Learning Scientist developing scalable AI systems for Teladoc Health. Collaborating with teams to enhance decision-making through machine learning and data analytics.

Posted 7/8/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $150,000 - $175,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing scalable machine learning systems, with a strong focus on production-quality software development using Python, Spark, and SQL. Capable of translating complex machine learning solutions into actionable business outcomes while ensuring robust model lifecycle management and effective communication with stakeholders.

Highest-signal resume keywords

Machine Learning Lifecycle ManagementProduction-Quality Software DevelopmentStatistical Modeling and Machine Learning AlgorithmsData Analysis and ExperimentationSystem Design for Scalable Machine Learning Solutions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData EngineeringPythonSparkSQLModel DeploymentStatistical AnalysisFeature EngineeringCI/CDModel Monitoring

Soft Skills

CollaborationCommunicationProblem-SolvingStakeholder EngagementAdaptability

Tools & Technologies

MLflowDatabricksDistributed ComputingReal-Time InferenceData Pipelines

Industry Keywords

SaaSConsumer TechnologyClinical OperationsBusiness OptimizationData Quality

Tech Stack

Tools & technologies

PythonSparkSQL

About the role

Key responsibilities & impact

Partner with Product, Engineering, Clinical, Operations, Marketing, and Data Engineering to design, build, deploy, and operate scalable machine learning and AI systems.
Own the end-to-end machine learning lifecycle: data and feature engineering through deployment, monitoring, experimentation, and continuous improvement.
Build production ready time series models to predict real time KPIs and optimize decision actions for clinical operations business optimization.
Propose, evaluate and interpret results for clinical, product and business decision-makers and own outcomes.
Collaborate closely with peers and stakeholders to 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 and machine learning pipelines following best practices.
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.
Monitor production models and data pipelines for data quality, feature drift, concept drift, latency, reliability, and business performance, proactively identifying and resolving issues.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Flexible Vacation Policy
80 hours of Paid Sick, Safe, and Caregiver Leave annually
Performance bonus and benefits (subject to eligibility requirements) listed here: Teladoc Health Benefits 2026