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About This Role
*Coursera and Udemy are now one company, creating one of the world's most comprehensive skills development platforms for the AI era. This strengthens our ability to accelerate AI\-powered innovation and shape how the world discovers and builds skills at a pivotal moment of change. Read more about the combined company by visiting our* *blog**.*
About Udemy
Udemy is an AI\-powered skills acceleration platform transforming how companies and individuals across the world build the capabilities needed to thrive in a rapidly evolving workplace. By combining on\-demand, multi\-language content with real\-time innovation, Udemy delivers personalized experiences that empower organizations to scale workforce development and help individuals build the technical, business, and soft skills most relevant to their careers. Today, thousands of companies, including Samsung SDS America, On24, Tata Consultancy Services, The World Bank, and Volkswagen, rely on Udemy Business for its enterprise solutions to build agile, future\-ready teams. Udemy is headquartered in San Francisco, with hubs across the United States, Australia, India, Ireland, Mexico, and Türkiye. Udemy recently combined with Coursera to create one of the world's most comprehensive skills development platforms.
Where we Work
Udemy is a global company headquartered in San Francisco, with additional U.S. offices in Denver and Austin, and international hubs in Australia, India, Ireland, Mexico, and Türkiye.
About your skills
You're a data scientist who lives at the intersection of product, experimentation, and storytelling. You know that a great A/B test is only the beginning — the harder, more interesting work is figuring out *why* a treatment moved (or didn't move) the metric, what it tells you about your customers, and what the team should try next. You're fluent in SQL, Python, and modern experimentation practice, and you have a strong product instinct that lets you turn ambiguous business questions into well\-framed analyses. You partner naturally with PMs, designers, engineers, and marketers; you're as comfortable shaping a roadmap conversation as you are debugging a tracking pipeline. You care about scaling your impact through better tooling, sharper metrics, and analytics that other people can actually use without you in the room.
About this role
This is a highly visible role on Udemy's Consumer Product Analytics team, embedded with the product, design, and engineering teams who own the top of our consumer funnel — from how learners discover Udemy, through landing pages and search, into the purchase path and checkout. The Staff Data Scientist, Product Analytics will be the senior analytics partner for a portfolio of discovery and conversion squads, including teams working on conversion rate optimization, SEO, marketing technology, and our homepage and landing experiences.
You will help these teams run a high\-velocity experimentation program, define the metrics and dashboards they make decisions with, and use causal and behavioral analysis to surface the next big opportunities in the funnel. Your insights will directly shape how millions of learners around the world experience Udemy's consumer marketplace and subscription products.
Success in this role will require a combination of strong communication and collaboration skills, sharp product sense, deep experimentation rigor, and a customer\-centric mindset. We are interested in building a diverse, collaborative, and fun environment. Come help us improve lives through learning!
What you'll be doing
- Serve as the embedded product data science partner for a cross\-functional area focused on discovery and conversion — including PMs, designers, engineers, and marketers across CRO, SEO, MarTech, and homepage/landing surfaces.
- Drive a high\-throughput experimentation program end\-to\-end: hypothesis development, metric and guardrail design, power analysis, test design (including geo, switchback, and CUPED\-style variance reduction where appropriate), readout, and meta\-analysis across portfolios of tests.
- Own the analytical strategy for your business area — define the KPIs, secondary metrics, and segmentations that the team uses to evaluate the funnel, and continuously raise the bar on how rigorously decisions get made.
- Build and maintain the dashboards and self\-service analytics that PMs, marketers, and leadership rely on for acquisition, conversion, and consumer subscriptions performance; ensure they are trusted, well\-documented, and resilient to upstream data changes.
- Use advanced analytics techniques (causal inference, regression, clustering, forecasting, segmentation) to characterize learner behavior across the funnel and uncover non\-obvious opportunities; conduct ad hoc analyses and causal studies for the team's most pressing open questions.
- Translate findings into clear, actionable narratives for senior product and business leaders — written readouts, presentations, and recommendations that move roadmaps.
- Partner with Data Engineering and Analytics Engineering to improve event instrumentation, data models, and pipelines that your business area depends on.
- Set the standard for analytical rigor on the team: review experiment designs and analyses from peers, mentor more junior data scientists, and contribute to shared frameworks, tooling, and best practices.
- Shape the longer\-term analytics roadmap and OKRs for your area in partnership with product and DS leadership.
What you'll have
- Bachelor's degree in a relevant technical field, or equivalent practical experience. Advanced degree a plus.
- 6\+ years of hands\-on Data Science experience (4\+ with a PhD), with significant time spent as an embedded product or growth data scientist in a consumer business. Experience supporting top\-of\-funnel, growth, CRO, SEO, or marketing surfaces is strongly preferred.
- Expert\-level SQL and Python; experience with Databricks or a similar cloud data warehouse.
- Deep, applied expertise in experimentation: experimental design, power analysis, A/B and multi\-arm testing, variance reduction, sequential testing, and at least working familiarity with quasi\-experimental and causal inference methods for when randomization isn't possible.
- Strong product sense — ability to translate ambiguous, open\-ended business questions into structured analyses and crisp recommendations, and to push back constructively when the data tells a different story than the team expected.
- Exceptional data storytelling and visualization skills, with a strong eye for narrative and usability. Experience with Tableau is a big plus.
- Experience building automated data pipelines with tools like Airflow and dbt, and working with GitHub and CI/CD code review processes.
- Track record of operating at a Staff level: scoping work across a business area independently, leading cross\-functional alignment, defining new metrics and frameworks, and raising the analytical bar for the people around you.
- Strong ownership and ability to work autonomously, while collaborating effectively with teams and colleagues across global time zones.
Posting Date: June 11th, 2026
Application Window: We anticipate the application window will be open until June 24th, 2026\. Based on business needs, this opportunity may remain posted beyond or closed before the anticipated application window.
### Why work here?
You'll grow here.
Learning is part of the job. You'll get full access to Udemy courses, a monthly UDay to invest in yourself, and a budget to spend on whatever helps you improve. Many people are diving into AI lately, but what you focus on is up to you.
AI is real here.
We use it in the way we learn and the way we work. You'll have the space and tools to experiment, apply, and get better at using AI in practical ways.
You'll own your work.
We trust people to lead, make decisions, and follow through. You don't need to wait for permission or layers of approval to have an impact.
You'll build with others.
We collaborate openly and shape ideas together. Everyone has a voice, and good thinking is welcomed from any direction.
You'll see your impact.
What you build helps people grow their skills, change their careers, or find a path forward. You've got the experience, why not use it to help others gain theirs?
*Bring your curiosity. We'll bring the platform and the support. Let's* *LEARN* *together.*
### Our Benefits Start with U
Our benefits start with you and were built to provide you and your family with the protection and care you need, making it easy to access the right coverage when you need it most. Benefits vary by region, and we encourage applicants to review our Australia Benefits, India Benefits, Ireland Benefits, Mexico Benefits, Turkiye Benefits \& US Benefits, pages to get an understanding of some of the benefits we offer. For details on region\-specific benefits, please refer to the information provided during the hiring process.
*Benefits outlined are provided as a general overview and may vary depending on the location, role, and employment classification. All benefits are subject to change at the discretion of the organization and in accordance with applicable laws and policies.*
*At Udemy, we value diversity and inclusion and consider qualified applicants without regard to race, color, religion, sex, national origin, ancestry, age, genetic information, sexual orientation, gender identity, marital or family status, veteran status, medical condition, or disability. We understand that not everyone will match each of the qualifications. However, we also realize that everyone has unique experiences that can add value to our company. Even if you think your background might not perfectly align, we'd love to hear from you!*
*To protect against recruitment fraud, Coursera \+ Udemy recruiters only communicate via official coursera.org/udemy.com email addresses and never through personal accounts. We do not accept resumes via email or social media; please submit all applications directly through our careers page. If you encounter suspicious recruitment activity, please report it via our* *Fraudulent Activity Submission Form**.*
*Information regarding data privacy is available within the* *Udemy Careers Privacy Notice**.*
Salary Context
This $156K-$195K 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 Udemy, 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: $156K to $195K.
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.
Udemy AI Hiring
Udemy has 1 open AI role right now. They're hiring across Data Scientist. Based in Austin, TX, US. Compensation range: $195K - $195K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 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.
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