The posting
Compensation Up to 3% equity and up to $250k in cash compensation.
What We Do Vantara is laying the foundation for self improving software. Our agent finds patterns in user behavior, rank orders issues in your products and proactively remedies them, finding hard to spot issues and opportunities. We aren’t publicly sharing more details about what we’re building just yet but would love to tell you more. You'll own direct relationships with these customers, delivering insights that drive real product decisions. This isn't traditional analytics - you're building the measurement infrastructure for the next generation of AI products.
What You’ll Do 70% Customer Champion: Build deep relationships with design partners, uncover what drives their success, and deliver insights that drive product changes for them. 20% Technical Builder: Create the next-gen methodology for generating user insights and productize it. 10% Product Strategist: Connect the dots between customer learnings and product vision. Your insights won’t sit in a deck - they’ll drive the roadmap.
Who We Are Team Raghav Sethi has built distributed systems that ran on ~70,000 machines at Meta. He was a tech/product lead for Presto - Meta’s primary SQL analytics engine. At Airtable, he worked across infrastructure and product engineering. He re-engineered the core database structures for 10x scale and led three major new product initiatives. He is obsessed with customer feedback, and shipped multiple #1 feature requests and personally triaged ~9000 feedback entries. His final role at Airtable was leading the AI team, where he launched Airtable’s first 5 AI features along with a new pricing model. Rohan Katyal has led 0 to 1 product launches and built systems that scaled to global impact. At Meta, he launched Meta Verified the company’s first subscription product, and helped scale it to millions in ARR in under a year. He also led business messaging growth on WhatsApp, taking it from early traction to over $1B ARR. At Yelp, he built the company’s experimentation platform, now an Harvard Business School case study. He also verticalized the local services marketplace, accelerating growth that now accounts for the majority of company revenue. His work spans establishing new products, growth, and monetization.
The founders have been close friends since undergrad.
Stage We have an initial version of the product deployed to a select group of design partners, and are co-building the product with them. We are backed by South Park Commons, Homebrew, and leaders from Anthropic, OpenAI, Meta, Sierra, Yelp, and Figma.
Logistics We are located in SF and work from the office 4 days/week. Candidates must have existing U.S. work authorization. We can sponsor visa transfers (e.g., H-1B), but cannot support new visa applications at this time.
Values Obsess over customers — we dive deep to understand their workflows and pain points. Work like it’s a marathon — we push hard, but pace ourselves for the long game. Detail-driven where it counts — we sweat the critical stuff and move fast on everything else. Live in the future — we’re building for the next generation of AI-native companies, we need to operate one step ahead of them.
Who You Are You are the kind of person that is interested in founding your own company but have been held back because you don’t yet have the right idea, cofounder or logistics in place. You have considered joining a very early-stage startup but decided that the risk-reward ratio isn’t worth it. We feel you - we think that early employees are critical to the company’s success and usually aren’t rewarded appropriately. We offer top-of-the-market equity grants and cash compensation in the top 10% of the market. We don’t think this is being generous - we think this is being fair to talented folks that have plenty of great options.
Requirements 3+ years of hands on data science experience in a high caliber product-analytics teams, preferably at a B2B SaaS company Advanced Python & SQL skills: pandas, NumPy, StatsModels/Scikit-learn, warehouse fluency (BigQuery, Snowflake) Expertise in data sampling techniques, including stratified sampling, bootstrapping, and extrapolation. Independently built and shipped analytics frameworks or tools adopted by product or growth teams Deep familiarity with causal inference and measurement techniques: A/B testing, diff-in-diff, propensity scoring, matched markets, statistical power analysis Practical experience with LLMs: prompt-engineering, embeddings, evaluation of hallucination risk Clear communicator: author executive level insights readouts for product, design, and engineering audiences Self-starter who thrives in ambiguity, prioritizes ruthlessly, and delivers projects end-to-end with minimal guidance Learns new tools and methods rapidly; consistently ships self-initiated improvements that cut analysis cycle-time
Process We will have 2 screening interviews, one in-person and one remote. These interviews will test real-world problem solving and should need no advance preparation. Finally, we will want to spend at least 2 days (and ideally more) working together on a real-world project. You will be compensated for your time.
