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About This Role
About Rundoo
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Our mission is to empower independent supply stores with best\-in\-class technology. Think of your local hardware store or mom\-and\-pop nursery—these are our clients. From paint to lumber to flooring, over 200,000 such stores across the country sell over $1T of building materials annually using outdated, on\-premises systems. We’re aiming to help them modernize so that they can continue to thrive.
Backed by leading investors including Bessemer and CRV, we've raised $18M across three rounds and are growing quickly. Our team is made up of builders, sellers, and industry veterans with a shared goal: to bring modern technology to an overlooked industry.
About the role
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You’ll own data science at Rundoo end\-to\-end: turning ambiguous business questions into crisp scopes, delivering robust analysis on predictable timelines, and building lightweight internal systems so insights are reproducible (not just a one\-off notebook). You’ll be a thought partner to leaders across GTM, product, and finance — shaping the question as much as answering it — and proactively surfacing anomalies and opportunities as the business scales.
This is a remote role with a strong preference for candidates based in SF or NYC. You will report directly to the Head of Data Science.
What you’ll do as an Data Scientist at Rundoo
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- Deliver high\-trust analysis on clear timelines: stakeholders trust both the answer and the ETA; assumptions and limits are explicit.
- Translate business problems into analysis \+ system requirements: turn vague asks into crisp scopes, metrics definitions, and data contracts.
- Build and maintain internal decision systems: ship lightweight tools/workflows so insights are reproducible and maintainable by others.
- Partner with GTM teams: help Sales/GTM move from “interesting analysis” to actions (e.g., prospecting lists, territory design, experimentation).
- Proactively surface issues: detect anomalies, broken assumptions, or misallocated spend without waiting for a ticket.
- Support fundraising readiness: contribute to an evergreen, credible data pool and reporting that leadership can rely on.
Requirements
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- 5\+ years of experience in data science, analytics, applied ML or MLE in a high\-growth environment
- Strong applied analytics / data science foundation (statistics, experimentation, causal thinking, forecasting, etc.).
- Demonstrated ability to scope ambiguous problems and drive to decisions with stakeholders.
- Comfort writing production\-quality code (especially Python) and building maintainable internal systems (not just notebooks).
- Excellent communication: can explain tradeoffs, assumptions, and recommendations to non\-technical audiences.
- High ownership and autonomy in a fast\-paced environment; can operate without a lot of process.
Bonus points
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- Experience partnering closely with Sales/GTM teams (pipeline, conversion, pricing, prospecting, territories, etc.).
- Experience building internal analytics tooling on cloud infrastructure (basic best practices, reliability, maintainability).
- Experience operating in early\-stage startups and/or building “v1 systems” that later scale.
- Strong “structured systems thinking” and willingness to defend or revise an approach under scrutiny.
Location
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- Remote friendly
- *Expect \~1 week of travel per quarter to either our Chicago or Redwood City offices.*
About the team
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You’ll partner closely with leaders across the business (GTM, product, and finance) and work directly with exec stakeholders. This is a high\-trust, cross\-functional role where the outputs need to be decision\-ready and reproducible. You would work directly with the GTM leaders on the team:
- Amrit (Data Science): Studied CS \& English at Cornell; Data Scientist at Enigma; Public Interest Technology Fellow at the New York Public Library. Enjoys mending, maintenance \& buying junk on eBay.
- Matt (Sales): Studied history at Northwestern; taught middle school in Chicago (hardest job ever), led sales for a consumer start\-up (Catch Co.), joined Rundoo as an AE and now leads the GTM team; former competitive angler in college (bass fishing )
- Vidhan (Client Experience): Studied Biomedical Engineering and Econ at Duke University; Formerly Director of Product at BuildZoom, Product lead at Hinge Health, and Co\-Founder of Urova Medical.
Interview Process
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- Intro call (30 min)
- Take\-home exercise overview (30 min)
- Paid Take Home (\~4\-6 hours) — This is the core of the interview. We’ll share more information about Rundoo and ask you to complete a task representative of the work you’d do at Rundoo. This interview will be paid, and we ask that you dedicate significant time to it.
- On\-site
+ Take\-home review \- pressure\-test the analysis; reproduce numbers; push on assumptions
+ GTM collaboration \- test trust/translation and move from analysis to action.
+ Lunch with the team
+ Problem solving discussion \- informal discussion on how problems are approached and tools used.
+ Systems/engineering \- Basic systems architecture and best practices for building an internal tool.
+ Culture \- Behavioral questions, geared toward our culture \& values.
- References: one manager, one peer, one stakeholder (PM/GTM/Finance).
Benefits
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- Full medical, dental, and vision coverage (100% of premiums for you, 50% for dependents)
- 401k with Betterment
- Unlimited PTO with 10 company paid holidays
- Gym allowance
- Daily team lunches for those in office
- Learning materials and audiobook subscriptions
- Dog\-friendly office in Redwood City \- https://rundoo.ai/rundogs/
About our founders
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- Andrew (CTO): studied computer science \& humanities at Stanford; worked as a software engineer and head of engineering at Apple \& Anova; danced with the SF ballet (where he met his wife)!
- Nick (CEO): studied math \& computer science at Stanford; worked as a trader at Bridgewater \& Citadel and as a PM at Google \& Enigma; distantly related to the founder of the Hershey company
How we've fundraised
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We've raised $18m across three rounds:
- A $2m pre\-seed led by Kent Bennett at Bessemer with participation from Plug \& Play, Quiet Capital, and Sequoia.
- A $5m seed led by Caitlin Bolnick Rellas at CRV.
- An $11m series A led again by Bessemer and CRV.
Compensation Range: $201,000 \- $248,000
Salary Context
This $201K-$248K range is above the 75th percentile 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 Rundoo, 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 ($224K) sits 13% above the category median. Disclosed range: $201K to $248K.
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.
Rundoo AI Hiring
Rundoo has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $248K - $248K.
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.
Frequently Asked Questions
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