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About This Role
At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.
WHOOP is hiring Data Scientists to embed across the product organization and drive decisions through rigorous measurement, experimentation, and causal inference. You'll partner with product teams across Healthcare, Algorithms, AI, Hardware, and other product areas to design experiments, build measurement frameworks, model user behavior, and translate complex analyses into clear recommendations that improve the member experience. You'll bring statistical rigor, product intuition, and intellectual curiosity to help teams make faster, smarter, and more impactful decisions.
### RESPONSIBILITIES
- Design and analyze experiments (A/B tests, quasi\-experiments, causal inference methods) to measure the impact of product changes on member outcomes.
- Build measurement frameworks and KPIs that quantify product success and surface opportunities for improvement.
- Partner with Product, Engineering, Research, Design, and cross\-functional leadership to inform strategy and roadmap decisions with data.
- Develop behavioral models and analyses that deepen our understanding of how members engage with WHOOP across their lifecycle.
- Build scalable, self\-serve reporting and tooling that lets your stakeholders make decisions without waiting on you.
- Proactively surface insights and recommendations that go beyond the ask — identify opportunities others haven't seen yet.
- Develop deep context on your product domain and use it to ask sharper questions, challenge assumptions, and push the product forward.
### ABOUT YOU
You might know this role as "product analyst," "decision scientist," or "analytics engineer" depending on where you've worked — titles vary wildly across the industry and we care about what you actually do, not what your last company called it. If you've owned metrics end\-to\-end, designed experiments, and turned ambiguous questions into clear recommendations that shipped product changes, you're who we're looking for.
- You've done this before: 2–6\+ years in a quantitative role where you owned business\-critical metrics or initiatives. (We're hiring across levels; we'll figure out the right fit together.)
- You use AI tools proactively and hold AI\-assisted work to the same bar as your own.
- You think in hypotheses: You can take a vague question, frame it as something testable, wrangle the right data, isolate insights, and communicate actionable recommendations to stakeholders who aren't data people.
- You're a strong storyteller: You know how to make a compelling, concise case for action.
- You're technically sharp: Advanced SQL is table stakes. Familiarity with modern data stacks (Snowflake, dbt, ELT workflows) and visualization tools (Hex, Sigma, Amplitude, Looker) is a plus. Python/R for statistical analysis is valued but not required.
- You have product sense: You think about the member experience, not just the numbers.
- You're curious about how things work. Algorithms, hardware, health science — you don't need to be an expert, but you should want to understand.
- You're a self\-starter who thrives in ambiguity and doesn't wait to be told what to work on.
### TEAMS WE'RE HIRING FOR
We're looking for multiple data scientists to work across our product teams. During the interview process, we'll work with you to find the right team fit based on your interests and strengths:
- AI Product: The future of intelligent coaching and personalized insights powered by AI/ML.
- Algorithms: Where the science meets the product — sleep, recovery, strain, and how algorithmic outputs translate into member value.
- Hardware Product: The physical device experience — sensors, wearability, and next\-gen hardware.
- Healthcare Product: Building WHOOP's Health Operating System — health outcomes, clinical features, personalized wellness.
*This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.*
*Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.*
*WHOOP is an Equal Opportunity Employer and participates in* *E\-verify* *to determine employment eligibility*
*The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.*
*At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long\-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long\-term growth and success.*
*The U.S. base salary range for this full\-time position is $100,000 \- $150,000 Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job\-related skills, experience, performance, and relevant education or training.*
*In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.*
*These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.*
Salary Context
This $100K-$150K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Whoop, 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 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($125K) sits 35% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Whoop AI Hiring
Whoop has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, MLOps Engineer. Based in Boston, MA, US. Compensation range: $140K - $300K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national median.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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