À propos
Translate ambiguous customer questions into well-scoped data science projects spanning panel data, time series, and causal inference Engineer features from large alternative-data panels (transaction-level, invoice-level, web-scraped) using Spark Build, validate, and interpret causal and predictive models that link alternative-data signals to financial outcomes such as revenue, earnings surprise, and KPI inflections Author technical white papers and customer analyses for institutional investors and Fortune 500 readers, including figures, equations, and narrative framing for sophisticated readers Use LLM coding assistants and agents as a primary collaborator: prototype faster, write higher-quality code, audit your own work, and ship deliverables in compressed timelines Build internal LLM-driven tooling (agents, eval harnesses, retrieval pipelines) for the broader organization Partner with data engineering, product, and revenue teams to close the loop between signal development and the customer-facing product Set technical standards for the team: PEP 8, type hints, vectorized operations, reproducible notebooks, sound methodology, and citation discipline Requirements:
You have shipped multiple data science projects end-to-end and can point to quantifiable customer or business impact Your statistical foundations are real, not surface-level. You can defend a causal-inference method choice end-to-end under technical questioning You write Python that reads like the work of a senior engineer: clear naming, type hints, vectorized code, no premature abstraction You already use LLM coding assistants daily and can describe specific cases where they multiplied your output without compromising quality, and specific cases where you overrode them Writing is a primary skill for you, not a side hobby. You communicate as carefully on the page as you do in code You take ownership without prompting: you find the right question, ship the answer, and explain it Humility about what you do not know matters as much as confidence about what you do You read the literature, cite sources, and ground your work in established methods rather than reinventing them. Benefits:
We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more! Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust.
Compétences linguistiques
- English
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