Data Scientist

YipitData isn’t a place for coasting - it’s a launchpad for ambitious, impact-driven professionals. From day one, you’ll take the lead on meaningful work, accelerate your growth, and gain exposure that shapes careers.

Why Top Talent Chooses YipitData:

• Ownership That Matters: You’ll lead high-impact projects with real business outcomes
• Rapid Growth: We compress years of learning into months
• Merit Over Titles: Trust and responsibility are earned through execution, not tenure
• Velocity with Purpose: We move fast, support each other, and aim high—always with purpose and intention

If your ambition is matched by your work ethic - and you're hungry for a place where growth, impact, and ownership are the norm - YipitData might be the opportunity you’ve been waiting for.

YipitData turns billions of alternative-data points into signals that institutional investors and Fortune 500 customers depend on for high-stakes decisions. The data science team owns the methods behind those signals. We do causal inference on panel data, predictive modeling against earnings outcomes, and the white papers customers read.

This role pairs applied data science with AI-native tooling. You will own substantive analytical projects end-to-end. You will use LLM coding assistants as a primary collaborator across every phase of the work: exploratory analysis, production code, written deliverables. You will also help shape how the team builds with AI-native workflows, including tool selection, guardrails, and evaluation patterns.

This is a remote-friendly opportunity that can sit in NYC (where our headquarter is located), one of our office hubs (Austin, Miami, Cupertino), or anywhere else in the US. 

• 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

• 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