Interested in this Data Scientist role at Yoodli AI Roleplays?
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About This Role
Who we are
Yoodli is a venture-backed company building an AI Platform for Human Skills, helping people get measurably better in their careers by supporting them in the flow of work.
Our team includes former employees from Google, Apple, Microsoft, Tableau, and beyond, united by a passion for empowering people using AI. We're headquartered in the heart of Seattle, and we're having a lot of fun along the way. Our culture is built on failing fast, iterating constantly, and taking bold bets. We value humility, a bias for action, and the belief that we win together.
We've raised $60M, including a $40M Series B led by WestBridge Capital, backed by Madrona, Neotribe, Cercano, and incubated at the Allen Institute for AI. We're valued at $300M and grew revenue 900% year over year.
Our customers include Google Cloud, Snowflake, Databricks, RingCentral, SAP, FranklinCovey, Korn Ferry, Sandler Training, Toastmasters, and many more, with learners in over 150 countries. Google Cloud certified 15,000+ reps without scaling headcount. Snowflake recovered 1,200+ manager hours per quarter. Harness cut training review time by 75%.
We've been featured in TechCrunch, Axios, The Information, Forbes, Fast Company, and more. Try the platform free at yoodli.ai.
What we are hoping you will bring
You believe in the mission and care deeply about helping people achieve more by communicating better. As our most senior data scientist, you will own the data function end to end: the infrastructure, the standards, and the judgment calls that decide what we build next.
This is a principal-level, high-autonomy role. You'll set the measurement strategy for a product whose core output is generated by LLMs, which means defining what "measurable skills development" means in numbers and then proving we move it. You'll be the person leadership turns to when a decision needs evidence, and the person the product team turns to when a metric looks wrong. You'll also help our customers quantify their own impact with Yoodli, which makes this work directly commercial.
You won't have a team to start, though you may build one over time as the role evolves. Either way, you'll raise the data bar for everyone here by example.
This role is hybrid with an expectation of 3 days a week in the office — you'll need to be based in the Greater Seattle Area.
What you'll do
- Own the measurement strategy for the platform: define the metrics that say whether learners are actually improving, and hold the company to them
- Design and run the experimentation program — A/B tests, holdouts, and rigorous LLM evaluation — and set the standards other teams build on
- Build the data foundation: pipelines, semantic layer, dashboards, and models that serve both internal decisions and customer-facing analytics
- Partner with product, design, and engineering to turn ambiguous questions into analyses that change what we ship
- Work directly with customers' BI and enablement teams so they can tell the story of impact with Yoodli inside their own organizations
- Build models that turn communication signal into product value — scoring, skill progression, benchmarking, and churn or expansion prediction
- Translate analysis into a clear recommendation for executives and the board, and defend it
- Set the technical bar for data at Yoodli: review others' analyses, mentor engineers and PMs on experiment design, and lay the groundwork for a team
What you'll bring
- 8+ years in data science, product analytics, or a closely related field, including time as the most senior data person on a product or business line
- Deep expertise in SQL and Python, and comfort shipping production-grade analytical code
- A track record of running an experimentation program, not just individual tests — power analysis, sequential testing, guardrail metrics, and the discipline to call a result null
- Experience evaluating LLM or other generative systems: building eval sets, designing rubrics, measuring quality when there is no clean ground truth
- Fluency with modern data stacks (dbt, Snowflake, BigQuery) and BI tooling (Looker, Mode, Hex, or similar), and the judgment to choose what a company this size actually needs
- Demonstrated influence on product strategy at the exec level — you can show a decision that went a different way because of your analysis
- Excellent communication skills, and the ability to be the clearest thinker in a room full of opinions
- Nice to have: speech analytics, NLP, or conversational AI; embedded or customer-facing analytics; early-stage startup experience
- Ability to be on-site at our Seattle,WA office M, T, and TH
Why Yoodli
- Mission-driven company helping millions improve how they communicate
- The data function is yours to define, with executive visibility from day one
- Collaborative, inclusive team with room to grow
- Competitive compensation and meaningful equity
- Base Salary: $200k – $220k
- Flexible work environment
- Startup culture
Salary Context
This $200K-$220K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Yoodli AI Roleplays, 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 $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($210K) sits 11% above the category median. Disclosed range: $200K to $220K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Yoodli AI Roleplays AI Hiring
Yoodli AI Roleplays has 1 open AI role right now. They're hiring across Data Scientist. Based in Seattle, WA, US. Compensation range: $220K - $220K.
Location Context
AI roles in Seattle pay a median of $226,100 across 1,045 tracked positions. That's 6% above 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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