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About This Role
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.
Teladoc Health Inc. seeks Data Scientist III (Multiple Openings) at its facility located at 155 E 44th Street, 17th Floor, New York, NY 10017\.
JOB DESCRIPTION:
Resolve and assess wide range of issues in creative ways and suggest variations in approach. Devise solutions based on limited information and precedents and adapt existing approaches to resolve issues. Work independently and review at critical points. Support detailed analyses, modelling, and forecasting for key operational areas ranging from marketing to finance to clinical operations and impact rapidly growing business. Collaborate closely with cross\-functional stakeholders, scope and solve problems faced, and help decision\-makers on the team form educated, practical strategies and recommendations. Proactively discover important trends in key metrics impacting business, distill key insights, present to key executives, and drive meaningful actions. Leverage a broad set of technical skills and strong business understanding to creatively solve complex problems with strategic impact. Provide insights about products and member experience to inform Product and Marketing teams for roadmap and feature decisions. Build model pipelines and data models that enable actionable visualizations and insights to business stakeholders. Demonstrate solid communication and stakeholder management skills and deliver complex projects in time. Telecommuting: 100% Remote.
REQUIREMENTS:
This position requires a bachelor’s degree or foreign equivalent in Data Science, Business Administration, Actuarial Science or a related field and 5 years of experience as a Data Scientist, Software Engineer or in an occupation involving SaaS or consumer companies. Additionally, the applicant must have employment experience with: (1\) End\-to\-end design, development, orchestration, and deployment of Artificial Intelligence models using MLOps tools (Git and containerization) to power business critical decisions while meeting regulatory requirements; (2\) Building predictive models and causal inference models using Python and Pyspark to predict user behavior and provide actionable strategies to manage user lifecycle; (3\) Development of comprehensive analytics solutions using Tableau and Grafana (Business Intelligence tools) to translate complex data into compelling data stories and actionable business strategies; (4\) Designing, orchestrating, and evaluating statistical experiments (hypothesis testing and A/B testing) to measure impact of AI models and identify causal relationships; and (5\) SQL and NoSQL programming combined with distributed computing framework to engineer scalable data pipelines and build proprietary data assets.
Alternatively, the employer will accept a master’s degree or foreign equivalent in Data Science, Business Administration, Actuarial Science or a related field and 3 years of experience as Data Scientist, Software Engineer or in an occupation involving SaaS or consumer companies. Additionally, the applicant must have employment experience with: (1\) End\-to\-end design, development, orchestration, and deployment of Artificial Intelligence models using MLOps tools (Git and containerization) to power business critical decisions while meeting regulatory requirements; (2\) Building predictive models and causal inference models using Python and Pyspark to predict user behavior and provide actionable strategies to manage user lifecycle; (3\) Development of comprehensive analytics solutions using Tableau and Grafana (Business Intelligence tools) to translate complex data into compelling data stories and actionable business strategies; (4\) Designing, orchestrating, and evaluating statistical experiments (hypothesis testing and A/B testing) to measure impact of AI models and identify causal relationships; and (5\) SQL and NoSQL programming combined with distributed computing framework to engineer scalable data pipelines and build proprietary data assets.
RATE OF PAY : $172,994\.00 \- $219,400\.00 per year
Applicants who are interested in this position should apply by emailing resume to [email protected] Reference Job Code: 10172156\_2026 or via https://www.teladochealth.com/careers/ and search for Data Scientist III
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.
Why join Teladoc Health?
- Teladoc Health is transforming how better health happens. Learn how when you join us in pursuit of our impactful mission .
- Chart your career path with meaningful opportunities that empower you to grow, lead, and make a difference.
- Join a multi\-faceted community that celebrates each colleague’s unique perspective and is focused on continually improving, each and every day.
- Contribute to an innovative culture where fresh ideas are valued as we increase access to care in new ways.
- Enjoy an inclusive benefits program centered around you and your family, with tailored programs that address your unique needs.
- Explore candidate resources with tips and tricks from Teladoc Health recruiters and learn more about our company culture by exploring \#TeamTeladocHealth on LinkedIn .
*As an Equal Opportunity Employer, we never have and never will discriminate against any job candidate or employee due to age, race, religion, color, ethnicity, national origin, gender, gender identity/expression, sexual orientation, membership in an employee organization, medical condition, family history, genetic information, veteran status, marital status, parental status, or pregnancy). In our innovative and inclusive workplace, we prohibit discrimination and harassment of any kind.*
*Teladoc Health respects your privacy and is committed to maintaining the confidentiality and security of your personal information. In furtherance of your employment relationship with Teladoc Health, we collect personal information responsibly and in accordance with applicable data privacy laws, including but not limited to, the California Consumer Privacy Act (CCPA). Personal information is defined as: Any information or set of information relating to you, including (a) all information that identifies you or could reasonably be used to identify you, and (b) all information that any applicable law treats as personal information. Teladoc Health’s Notice of Privacy Practices for U.S. Employees’ Personal information is available* *at this link* *.*
Salary Context
This $172K-$219K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Teladoc Health, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $172K to $219K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Teladoc Health AI Hiring
Teladoc Health has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span New York, NY, US, Remote, US. Compensation range: $219K - $230K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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