Talent Analyst
Luma AI
People & HR, IT
London, UK · Seattle, WA, USA
USD 142,500-212,500 / year + Equity
Posted on Feb 4, 2026
Talent Analyst
Palo Alto, CA • London, UK • Seattle, WA
Recruiting & People
Hybrid
Full-time
Team: Talent Analytics & Operations (TAO)
Location: Palo Alto, CA (hybrid preferred). Open to Seattle, WA, potentially London, UK.
The Team
Talent Analytics & Operations (TAO) exists to make Luma's growth engine intelligent. We build the data and systems that let Luma hire world-class researchers and engineers faster than anyone else in AI.
We're a small team of builders, not administrators. You'll be the first dedicated analytics hire, working alongside ops and automation specialists and partnering with recruiting leads across Research, Applied Research, Product Engineering, GTM, and Business Operations.
Is this role for you?
Yes, if you:
- Get excited when you find a pattern in messy data that nobody else noticed
- Have built a dashboard or report that actually changed how someone made a decision
- Want to define new metrics, not just report on existing ones
- Prefer shipping something imperfect over planning something perfect
- Want to own problems end-to-end, not wait for someone to hand you requirements
- Are energized by ambiguity and fast pace
Probably not, if you:
- Want a well-defined role with clear boundaries and established processes
- Prefer deep specialization over breadth
- Need a large team and extensive onboarding to be effective
- Are looking for a pure data science or ML role
- Want to work on one type of problem for a long time
A Day in the Life
You start the morning in a working session with the Head of Recruiting, sketching out how to measure "quality of hire." It's a metric everyone wants but nobody agrees on. You're mapping out what signals we could actually capture (hiring manager satisfaction at 30/90 days, time to first meaningful contribution, performance review correlation) and what data infrastructure we'd need to make it real.
Mid-morning, you're building a draft schema for a lightweight data warehouse. Our ATS and HRIS don't talk to each other natively, and you're tired of stitching together exports manually. You're evaluating whether we need a proper ETL pipeline or if a simpler solution gets us 80% of the value.
After lunch, you're presenting to leadership. Not a status update, but a recommendation: our Research hiring funnel converts 3x better than Product Engineering, and you've got a hypothesis why. You're proposing an experiment to test it.
Late afternoon, you're researching how other companies measure recruiter effectiveness. Most of the industry uses activity metrics (screens per week, submits per role). You think that's backwards. You're drafting a framework for outcome-based measurement that could become how Luma thinks about recruiting performance, and maybe something we share externally.
You end the day automating a report that used to take someone two hours every Monday. Small win, but it adds up.
What you'll own
Recruiting analytics and reporting
- Build and maintain dashboards that leadership actually uses (weekly, without anyone asking)
- Reduce recruiting leadership's manual reporting time by 80%
- Enable hiring managers to self-serve pipeline data in real-time
- Create the metrics and visualizations the CEO looks at weekly
Data infrastructure
- Design and build lightweight data infrastructure (warehouse, pipelines, integrations) to support analytics at scale
- Connect fragmented systems (ATS, HRIS, scheduling tools) into a unified data layer
- Own data quality, reliability, and documentation so the team can trust what they're seeing
New metrics and frameworks
- Define metrics that don't exist yet at Luma (quality of hire, source effectiveness, time to productivity)
- Partner with recruiting leads and hiring managers to understand what they need to measure and why
- Research industry best practices, then build something better
- Create measurement frameworks that could become how Luma thinks about talent, and potentially how the industry does
Insights and recommendations
- Translate data into "so what" and "now what" for non-technical stakeholders
- Identify trends, anomalies, and opportunities that inform recruiting strategy
- Answer the questions leadership doesn't know to ask yet
Automation and workflows
- Automate repetitive data pulls, report generation, and cross-system workflows
- Build lightweight internal tools when off-the-shelf solutions don't fit
- Connect data sources to reduce manual entry and human error
What success looks like
- 90 days: You've shipped a dashboard that leadership uses without being prompted. You've mapped the current data landscape and identified the biggest gaps. You've found at least one metric everyone assumed was accurate, realized it wasn't, and fixed it. Recruiting leads are coming to you directly because you're faster than digging through the ATS.
- 6 months: You've automated 5+ hours/week of manual reporting. You've built or started building a data infrastructure that connects our fragmented systems. You've surfaced an insight that changed a hiring decision or strategy. Leadership cites your data in board prep without you being in the room.
- 12 months: The internal data home exists and teams actually use it. You're answering "what should we do?" not "what happened?" You've created frameworks for metrics like quality of hire and time to productivity that are genuinely novel, and other companies are asking how we measure what we measure. You're telling me we need to hire someone to help you.
Who you are
You're a data person who's figured out how to make insights land. You write SQL without thinking about it. You've built dashboards or reports that people actually used to make decisions. You can explain a data quality issue to a recruiter and a funnel trend to a CEO.
You've built things, not just analyzed things. Maybe you've stood up a lightweight data warehouse. Maybe you've designed a schema that made reporting actually work. You understand that good analytics requires good infrastructure, and you're not afraid to build it.
You think about measurement as a craft. You're not satisfied with vanity metrics or industry defaults. You want to figure out what actually matters, how to capture it, and how to make it useful. You've probably been frustrated by how most companies measure things.
You've worked somewhere fast. Startups, high-growth companies, or scrappy teams where priorities shift and you figure it out. You're not waiting for perfect requirements. You're shipping and iterating.
You're comfortable with ambiguity. We're handing you problems, not specs. You'll need to figure out what question we're actually trying to answer, find (or build) the data, and present something useful.
You communicate clearly. You can turn a messy analysis into a clean story. You know when to caveat and when to be direct. You're not precious about your work. You'd rather be right than look smart.
What we're looking for
Must have:
- 2-4 years working with data in a business context (analytics, ops, BI, or similar)
- Strong SQL skills (complex queries, joins, window functions without Googling)
- Experience building dashboards or reports that informed real decisions
- Some experience with data infrastructure (databases, pipelines, ETL concepts)
- Comfort with messy, imperfect data and the judgment to know what to fix vs. ignore
- Experience at a startup or high-growth company (you know the pace)
- Clear written and verbal communication
Strong bonus:
- Python for data work (pandas, notebooks, API integrations)
- Experience designing or maintaining a data warehouse (even a lightweight one)
- Experience with recruiting, HR, or people data specifically
- Exposure to, ideally worked strongly with, AI-assisted tools and workflows (using Claude, ChatGPT, or similar for data work)
- Familiarity with ATS/CRM systems (Gem, Greenhouse, Lever, Ashby, etc.)
- Experience with automation tools (Zapier, n8n, Make)
- People management experience
Why this role is different
You're a builder, not a service desk. TAO owns products, not tickets. You're building infrastructure and frameworks, not running reports on request.
You're defining the metrics, not just reporting them. Most analyst roles execute. You'll decide what Luma should measure in the first place.
You'll build infrastructure, not just dashboards. We need someone who can stand up the data layer, not just query it.
You'll shape how the industry thinks about talent. We want to be the most data-driven people org in AI. The frameworks you build here could become how other companies measure what matters.
You're an early hire. You'll shape how Luma measures talent from the ground up. The patterns you establish will scale with the company.
Interview process
- Short screen: Intro call to get to know each other
- Take-home data task: You'll clean a messy recruiting dataset, build a simple analysis, and present one insight.
- Live working session: Walk us through your approach, iterate together
- Team/culture fit: Meet folks you'd work with day-to-day
FAQ
What tools will I use? Our current stack includes Gem (ATS/CRM), Ashby, Notion, Slack, Google Workspace, Metaview, and Zapier. For analytics, you'll likely work with SQL, Python, and whatever visualization tools make sense (we're flexible). For internal tools, we use n8n, Zapier, and increasingly AI-assisted development.
Is this a data analyst or data engineer role? Closer to analyst in scope, but with an engineering mindset. You're not building a data warehouse from scratch, but you will own data models, work with APIs, and build systems - not just reports.
Do I need to know how to code apps? Not required, but it's a big plus. The primary focus is analytics. The secondary opportunity is building internal tools. If you can do both, you'll have more impact.
What's the team structure? You'll report to Josh Gill (Talent Engineering & Operations) and work closely with recruiting leads across Research, Applied Research, Product Engineering, GTM, and Business Operations.
Is this remote? Hybrid in Palo Alto is ideal. We're also open to Seattle, potentially London, or remote within the US (West Coast preferred).
Compensation
- Location: Palo Alto hybrid strongly preferred (in-office 3 days/week). Open to Seattle, London, or remote US West Coast for exceptional candidates.
- Base salary (Tier 1 locations: SF Bay Area, NYC, Seattle): $142,500 – $212,500
- Equity: Yes, meaningful early-stage equity
- Benefits: Medical, dental, vision, 401(k), flexible PTO, hybrid flexibility
Luma is committed to providing reasonable accommodations throughout the interview process for candidates with disabilities. Please let us know if you need any accommodations.
About Luma AI
Luma AI builds multimodal AI to expand human imagination. Our flagship product, Dream Machine, lets anyone create stunning videos from text and images. In late 2025, we released Ray 3, the world's first reasoning video mode.
We're ~150 people, $4B valuation, backed by a16z, Amazon, AMD, and NVIDIA. We raised $900M in late 2025. We're scaling to 400+ and need to build the recruiting infrastructure to get there.
This is a rare chance to join a generational AI company early and build something that matters. The team here is smart, determined, and genuinely talented - but we also want to enjoy the ride together. We work hard because we care about what we're building, not because someone's watching the clock.
Compensation
The base pay range for this role is $142,500 – $212,500 per year.
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Req ID: R100088