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About This Role
OUR MISSION
We exist to create a more connected, compassionate, and confident experience for people with cancer and those who care for them. We make it easier to get answers, access high\-quality care quickly, and feel supported throughout treatment and beyond.
Today, Thyme Care is a market\-leading value\-based oncology care enabler, partnering with national and regional health plans, providers, and employers to deliver better outcomes and lower costs for thousands of people across the country. Our model combines high\-touch human support with powerful technology and AI to bring together everyone involved in a person’s cancer journey: caregivers, oncologists, health plans, and employers.
As a tech\-native organization, we believe technology should strengthen the human connection at the center of care. Through data science, automation, and AI, we simplify complexity, improve collaboration, and help care teams focus on what matters most: supporting people through cancer.
Looking ahead, our vision is bold: to become a household name in cancer care, where every person diagnosed asks for Thyme Care by name. If you’re inspired to make cancer care more human and to help reimagine what’s possible, we’d love to meet you. Together, we can build a future where every person with cancer feels truly cared for, in every moment that matters.
WHAT YOU’LL DO
As a Senior Data Scientist, you will join our Data team to help us build and maintain strong analytic capabilities supporting Thyme Care’s mission. You will model data, create visualizations to report to stakeholders, and deliver insights that drive action.
In this position, you will partner with our Product, Engineering, Operations, and Data Science teams to define and lead improvements to our data pipelines, setting direction that enables faster, more effective care delivery and measurement. You will own efforts to reduce latency between our data and applications, designing and building a scalable data architecture and healthcare data models that enable our pharmacy interventions and provider incentive programs.
- Gain a deep understanding of our core data architecture and lead improvements to our data pipelines, balancing near\-term needs with long\-term architectural health.
- Own and lead the development of data models that make it possible to analyze the nuances of our EMR drug delivery and drug economics in ways that scale across contracts and pharmacy interventions.
- Establish key KPIs and reporting standards that help stakeholders make our operations more efficient.
- Partner with stakeholders to understand and translate their business requirements into technical plans. This involves developing data models, pipelines, and analytics dashboards. For example, you own the end\-to\-end data life cycle of data ingestion from a practice, standardization and cleaning, core modeling and BI dashboarding for both internal and external partners.
- Lead the development of utilization and quality metrics and serve as a domain expert for stakeholders across the company. Additionally, you will leverage our data assets for business opportunities and strategic initiatives at Thyme Care.
WHAT YOU’VE DONE
- Deep experience working with healthcare data such as electronic health records, claims, or prior auth.
- Expertise in analytics, data modeling, and data transformation, and have used that expertise to influence decisions beyond your immediate scope.
- Deep expertise in dbt and SQL, with experience designing, rebuilding, and owning complex data models and pipelines that ensure reliability and scalability across the data platform.
- Experience with large healthcare datasets, ideally in a healthcare\-focused technology startup, health plan, or provider group with mature data structures and pipelines.
- A working knowledge of Python or R and experience with Looker or similar BI tools for data analysis and visualization is a plus.
- Previous direct involvement drug or Medicare Part D data is a plus.
WHAT LEADS TO SUCCESS
Act with our members in mind. Thyme Care’s mission, and in particular, our member experience, matters deeply to you.
Move with purpose. You’re biased to action. You know how to identify and prioritize your initiative’s needs and do what it takes to ensure that urgent and important needs are acted on immediately.
Seek diverse perspectives. You are humble and actively seek feedback from others, eager to learn and share knowledge.
Experience. You have worked with large healthcare datasets, ideally in a health plan or healthcare\-focused technology startup with mature data structures and pipelines as an Analytics Engineer or Data Engineer. Experience with medical claims, pharmacy claims, eligibility files, and other relevant healthcare data is essential for building data marts for reporting and analysis.
Technical ability. You have deep expertise in analytics, data modeling, and data transformation, with strong proficiency in dbt and SQL to build and maintain data models and pipelines. A working knowledge of Python or R and experience with Looker or similar BI tools for data analysis and visualization is a plus.
Clear communication. You can effectively convey your thoughts and ideas to both technical and non\-technical colleagues and stakeholders.
Comfort with ambiguity. You have a successful track record working at scaling organizations, in fast\-paced environments, and at ambitious startups. You navigate through challenges and find solutions in uncertain situations.
OUR VALUES
*At Thyme Care, our core values guide us in everything we do: Act with our members in mind, Move with purpose, and Seek diverse perspectives. They anchor our business decisions, including how we grow, the products we make, and the paths we choose—or don’t choose.*
*Our salary ranges are based on paying competitively for our size and industry, and are one part of the total compensation package that also includes equity, benefits, and other opportunities at Thyme Care. Individual pay decisions are based on several factors, including qualifications, experience level, skillset, and balancing internal equity relative to other Thyme Care employees. The base salary for this role is $166,500\-$185,000\. The salary range could be lower or higher than this if the role is hired at another level.*
*We recognize a history of inequality in healthcare. We’re here to challenge the status quo and create a culture of inclusion through the care we give and the company we build. We embrace and celebrate a diversity of perspectives in reflection of our members and the members we serve. We are an equal\-opportunity employer.*
*Be cautious of* *recruitment fraud**, and always confirm that communications are coming from an official Thyme Care email.*
Salary Context
This $166K-$185K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At thyme care, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 11% below the category median. Disclosed range: $166K to $185K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
thyme care AI Hiring
thyme care has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $185K - $240K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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