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# Staff, Data Scientist

- **Company:** Teladoc, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $200,000 - $215,000
- **Contract:** Permanent contract
- **Skills:** Big Data, Computer Programming, Information Engineering, Distributed Data Store, Python (Programming Language), Machine Learning, SQL Databases, Data Processing, Apache Spark, Information Technology, Modeling and Simulation, Software Version Control, Unsupervised Learning
- **Published:** November 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4049b136fe545a59

## About the Role

* Bachelor's degree in Computer Science, Engineering, Business Management, or a related field.
 * 8+ years of experience experience applying machine learning, statistics, and quantitative methods to solve complex business or healthcare problems.

 * Strong experience with data processing and analysis using SQL and distributed data-processing technologies such as Spark.

 * Strong programming skills in Python, Scala, R, or equivalent languages used for data science and machine learning.

 * Experience designing, developing, evaluating, and communicating machine learning or statistical solutions.

 * Demonstrated ability to independently lead complex analytical projects involving multiple stakeholders.

 * Strong understanding of machine learning, statistical inference, experimentation, and analytical methodology.

 * Ability to communicate technical concepts, results, limitations, and recommendations effectively to technical and non-technical audiences.

 * Experience mentoring or providing technical guidance to other data scientists.

 * Advanced degree in a STEM field or equivalent experience and expertise. Travel: Up to 10%, subject to business needs.
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## Description

The Staff Data Scientist is a senior individual contributor responsible for leading complex data science initiatives and developing analytics and algorithmic solutions that address high-impact business and healthcare problems.

The role applies advanced data science techniques, including supervised and unsupervised machine learning, statistical inference, causal modeling, and large-scale data analysis. The Staff Data Scientist works across multiple initiatives, provides technical guidance and mentorship to other data scientists, and partners with engineering, product, clinical, and business stakeholders to translate complex problems into effective analytical and machine learning solutions.

Essential Duties and Responsibilities

 * Lead complex data science, machine learning and AI initiatives of significant scope, operating with a high degree of independence.

 * Design, develop, and maintain analytics pipelines and algorithmic data products that address complex healthcare and business problems.

 * Apply advanced analytical methods, including supervised and unsupervised learning, statistical inference, causal modeling, and other appropriate quantitative techniques, to generate actionable insights.

 * Translate ambiguous business, clinical, and product problems into clearly defined analytical and machine learning approaches.

 * Provide technical leadership throughout the lifecycle of data science initiatives, from problem formulation and exploratory analysis through model development, validation, implementation, and ongoing improvement.

 * Establish and promote data science best practices, including reproducible analysis, appropriate experimental design, version control, peer review, documentation, and sound model-development practices.

 * Serve as a technical resource for other data scientists by providing mentorship, coaching, design guidance, and code and model reviews.

 * Partner with Data Engineering, ML Engineering, Product, Clinical, and business stakeholders to ensure analytical solutions appropriately address business needs.

 * Communicate complex analyses, methodologies, trade-offs, results, and recommendations clearly to technical and non-technical audiences.

 * Develop influential presentations and recommendations that enable stakeholders to make data-informed decisions.

 * Identify opportunities to improve existing analytical approaches, reusable methodologies, tools, and data science capabilities.

 * Manage multiple complex initiatives while keeping stakeholders informed of progress, risks, dependencies, and outcomes.

Contribute to technical standards and improve the quality, maintainability, and reproducibility of data science work across the broader team.
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