Data Engineer
Economic Data Scientist
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The dataset is the economy itself: 693M+ companies live, and largely unexplored. Most careers in data run on a sample, a slice of someone else's collection; here the whole population is the daily working material. The role on top of it has no clean name. Part forward deployed engineer: proof-of-concepts built for a live prospect, on their timeline, iterated until they convince. Part quant: hunting alpha in the raw, mostly in our own data, occasionally in a new source you'll integrate into the graph. Part applied data scientist, where applied is literal: hands in the data every day, cleaning it, checking it, rebuilding on top of it, until it serves the purpose. Full ownership throughout, you take the questions clients bring, dig for the answer, and defend what you find.
Who you are
- You own your work like a founder owns a product.
- A growth mindset, able to capitalize on unprecedented contexts through your skills and abilities.
- A strong problem solver, visible in the way you deal with the tension between brief and shipping.
- Resilient, especially in front of failure, the kind that always comes paired with pioneering work.
- An appetite to grapple with a variety of technical challenges.
What you'll do
- Build proof-of-concepts around a live prospect's case: assemble our data and tools into something they can hold, and iterate, ten or thirty times, until it wins.
- Hunt through a firm-level dataset of 693M+ companies for the pattern that answers a prospect's actual question: the insight nobody has looked for, that lands the deal.
- Turn a client's question into an analysis: frame the problem, choose the method, iterate until it convinces, and defend the conclusion out loud, in front of the client.
- Treat cleaning and validation as first-class work: the data is real, straight from the web, and yours to make dependable.
- Ship on client timelines: requests come in fast, answers go out in days, and you see them put to use.
- Work the full range of the seat: quality deep-dives, new sources worth bringing into the graph, findings that become published stories.
What we look for
- Statistical grounding solid enough to tell a real correlation from noise, and to defend the difference.
- The instinct to interrogate the data before trusting it, and the pace to ship a solid analysis this week rather than a perfect one next month.
- Comfort processing genuinely large datasets, tools-agnostic: SQL, Python, R, Scala, Spark, Excel, whatever gets it done.
- The resilience to iterate dozens of times on the same problem, including the restarts a client's request forces, without losing the thread or the pace.
- Comfort being the technical person in the room with a client: answering live, and saying "I'll check" when that's the true answer.
- Real curiosity about how businesses and markets behave, whether you got here through economics, computer science, or somewhere else entirely.
What we offer
- Fair, market-aligned base, ESOP tied to impact (for permanent roles), and a high salary growth rate tied to performance.
- Decisions that would need three approvals elsewhere are yours to make, and yours to answer for.
- Growth at the pace you can take, and responsibility that expands as fast as you prove you can hold it.
- What you build lands in front of some of the largest companies in the world, often within weeks.
- Steep learning curve, no matter how experienced you are, with people who've climbed it a desk away when you're stuck.
What we expect in return
- High tolerance for ambiguity, marked by your ambition to push forward with incomplete information.
- High speed and uncompromising quality in your work.
- The ability to quickly and effectively evaluate technical tradeoffs and translate them into relevant scenarios
- Genuine aversion to any customer or colleague struggling with something you delivered.
- Ask what would make it ten times better, starting with your own work.
We currently work in a hybrid setup, with 2 days remote / week. The 3 months probation period is fully onsite.
Это сохранённая копия объявления, опубликованного в другом месте. Вакансии снимают без предупреждения — перед откликом проверьте сайт работодателя. mentors.coach не является нанимающей стороной.