Senior Machine Learning Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Collaborate with Product, Engineering, Clinical, Operations, Marketing, and Data Engineering teams to design, build, deploy, and maintain scalable machine learning and AI systems.

• Manage the complete machine learning lifecycle: from data and feature engineering to deployment, monitoring, experimentation, and continuous improvement.

• Develop production-ready time series models to predict real-time KPIs and enhance decision-making for clinical operations optimization.

• Propose, assess, and interpret outcomes for clinical, product, and business leaders while taking ownership of results.

• Work closely with colleagues and stakeholders to clarify requirements for problem definitions, product features, and architecture, aiming to improve clinical outcomes through insights and models.

• Create modular, well-tested, production-quality software using Python, Spark, and SQL to construct scalable data engineering and machine learning pipelines in accordance with best practices.

• Ensure effective model lifecycle management through model versioning, MLflow, automated testing, CI/CD, and production monitoring.

• Develop and enhance scalable Spark and Databricks workloads, utilizing distributed computing best practices for large-scale data processing and real-time inference.

• Oversee production models and data pipelines to ensure data quality, feature drift, concept drift, latency, reliability, and business performance, actively identifying and addressing issues.

⛳️ Requirements

• Over 8 years of experience as a Machine Learning Scientist, Data Scientist, or in a comparable role within SaaS or consumer technology organizations.

• A Master's degree or higher in computer science, operations research, machine learning, information systems, engineering, or a related discipline.

• Proven experience in developing clean, robust, and reusable production-quality code using Python, Spark, and SQL.

• Extensive background in designing, building, and operating production machine learning systems, including scalable software, distributed data processing, reusable feature engineering pipelines, model deployment, monitoring, and ongoing improvement.

• Strong grasp of statistical modeling, machine learning algorithms, experimentation, model evaluation, forecasting, and explainability techniques, with the capability to select suitable approaches based on business and technical constraints.

• Excellent data analysis capabilities and a proactive approach to delivering, measuring, and iterating through experimentation and statistical analysis.

• Strong system design abilities, with the competence to architect scalable, maintainable, and observable machine learning solutions.

• Ability to translate machine learning solutions into quantifiable business outcomes and effectively communicate technical decisions, trade-offs, and anticipated value to both technical and business stakeholders.

🏝️ Benefits

• 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