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About This Role
About HappyRobot
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HappyRobot is the infrastructure for enterprises to build and orchestrate AI workforces. Our AI workers don't just communicate \- they make decisions, take action, and run operations autonomously across voice, email, and enterprise systems. Born in Y Combinator (S23\) and backed by a16z and Base10 with over $60M raised, we power critical operations for global enterprises worldwide.
Our platform is battle\-tested in the most demanding environments \- where AI has real consequences. We started in logistics, built our own voice stack, models, and orchestration layer from the ground up, and are now bringing that infrastructure to every enterprise that runs the real economy. Learn more about our vision in our manifesto.
About the Role
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You’ll help make data a core part of how we build and improve HappyRobot’s products.
You’ll work closely with Product, Engineering, and Machine Learning teams to measure how changes to our models, agents, and product features affect real\-world performance. You’ll define meaningful metrics, design experiments, and conduct deeper analyses to understand how our agents create value for clients.
Your work will range from evaluating A/B tests and model changes to analyzing millions of conversations and workflows. You’ll turn complex data into clear insights that influence our product and ML roadmaps.
What You’ll Do
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- Define and track product, feature, and agent\-level metrics.
- Design, run, and interpret A/B tests for model changes, prompts, agent behavior, workflows, and product features.
- Measure how the performance of our agents affects client outcomes, such as task completion, operational efficiency, response quality, and automation rates.
- Connect offline model evaluations with production performance and real\-world customer impact.
- Conduct deep analyses across conversations, workflows, and product usage to identify opportunities and explain differences in performance.
- Investigate anomalies and regressions, perform root\-cause analyses, and recommend improvements.
- Build statistical models, simulations, and analytical frameworks to support product and ML decisions.
- Partner with Engineering to improve instrumentation, data quality, experimentation systems, and analytical data models.
- Build dashboards and self\-serve tools that help teams understand product and agent performance.
- Communicate findings and recommendations clearly to technical and non\-technical stakeholders.
Must Have
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- 4\+ years of experience in Data Science, Product Analytics, or another highly quantitative product role.
- Strong experience with experimental design, A/B testing, statistics, causal inference, and hypothesis\-driven analysis.
- Advanced proficiency in SQL and Python.
- Experience defining and operationalizing product and feature metrics.
- Ability to translate ambiguous product questions into rigorous analyses and actionable recommendations.
- Strong product instincts and the ability to distinguish statistical significance from meaningful product or customer impact.
- Experience partnering closely with Product, Engineering, or Machine Learning teams.
- Strong written and verbal communication skills.
- High attention to detail and commitment to analytical accuracy.
- Founder mindset: ownership, independence, curiosity, and willingness to go deep.
Nice to Have
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- Experience working with large language models, AI agents, generative AI, or other probabilistic ML products.
- Experience measuring the production impact of model, prompt, retrieval, or orchestration changes.
- Familiarity with ML evaluation systems and the relationship between offline evaluations and online metrics.
- Experience analyzing conversational, NLP, speech, or other unstructured data.
- Experience with enterprise or B2B products.
- Experience combining quantitative analysis with qualitative methods such as conversation reviews, customer feedback, surveys, or user research.
- Familiarity with modern analytics infrastructure, data warehouses, experimentation platforms, and business intelligence tools.
- Prior experience in a fast\-growing startup or other highly ambiguous environment.
Why join us?
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- Opportunity to work at a high\-growth AI startup, backed by top investors.
- Rapidly growing and backed by top investors including a16z, Y Combinator, and Base10\.
- Ownership \& Autonomy \- Take full ownership of projects and ship fast.
- Top\-Tier Compensation \- Competitive salary \+ equity in a high\-growth startup.
- Comprehensive Benefits \- Healthcare, dental, vision coverage.
- Work With the Best \- Join a world\-class team of engineers and builders
Our Operating Principles
*Extreme Ownership*
We take full responsibility for our work, outcomes, and team success. No excuses, no blame\-shifting — if something needs fixing, we own it and make it better. This means stepping up, even when it’s not “your job.” If a ball is dropped, we pick it up. If a customer is unhappy, we fix it. If a process is broken, we redesign it. We don’t wait for someone else to solve it — we lead with accountability and expect the same from those around us.
*Craftsmanship*
Putting care and intention into every task, striving for excellence, and taking deep ownership of the quality and outcome of your work. Craftsmanship means never settling for “just fine.” We sweat the details because details compound. Whether it’s a product feature, an internal doc, or a sales call — we treat it as a reflection of our standards. We aim to deliver jaw\-dropping customer experiences by being curious, meticulous, and proud of what we build — even when nobody’s watching.
*We are “majos”*
Be friendly \& have fun with your coworkers. Always be genuine \& honest, but kind. “Majo” is our way of saying: be a good human. Be approachable, helpful, and warm. We’re building something ambitious, and it’s easier (and more fun) when we enjoy the ride together. We give feedback with kindness, challenge each other with respect, and celebrate wins together without ego.
*Urgency with Focus*
Create the highest impact in the shortest amount of time. Move fast, but in the right direction. We operate with speed because time is our most limited resource. But speed without focus is chaos. We prioritize ruthlessly, act decisively, and stay aligned. We aim for high leverage: the biggest results from the simplest, smartest actions. We’re running a high\-speed marathon — not a sprint with no strategy.
*Talent Density and Meritocracy*
Hire only people who can raise the average; ‘exceptional performance is the passing grade.’ Ability trumps seniority. We believe the best teams are built on talent density — every hire should raise the bar. We reward contribution, not titles or tenure. We give ownership to those who earn it, and we all hold each other to a high standard. A\-players want to work with other A\-players — that’s how we win.
*First\-Principles Thinking*
Strip a problem to physics\-level facts, ignore industry dogma, rebuild the solution from scratch. We don’t copy\-paste solutions. We go back to basics, ask why things are the way they are, and rebuild from the ground up if needed. This mindset pushes us to innovate, challenge stale assumptions, and move faster than incumbents. It’s how we build what others think is impossible.
*The personal data provided in your application and during the selection process will be processed by HappyRobot, Inc., acting as Data Controller.*
*By sending us your CV, you consent to the processing of your personal data for the purpose of evaluating and selecting you as a candidate for the position. Your personal data will be treated confidentially and will only be used for the recruitment process of the selected job offer.*
*In relation to the period of conservation of your personal data, these will be eliminated after three months of inactivity in compliance with the GDPR and legislation on the protection of personal data.*
*If you wish to exercise your rights of access, rectification, deletion, portability or opposition in relation to your personal data, you can do so through [email protected] subject to the GDPR.*
*For more information, visit* *https://www.happyrobot.ai/privacy\-policy*
*By submitting your request, you confirm that you have read and understood this clause and that you agree to the processing of your personal data as described.*
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 HappyRobot, 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.
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.
HappyRobot AI Hiring
HappyRobot has 1 open AI role right now. They're hiring across Data Scientist. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 3,708 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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