Data Scientist - Growth

As a Data Scientist for Growth at Legora, you'll build the data foundations that help our go-to-market teams make better decisions. You'll work end-to-end: connecting data from business systems, building reliable pipelines and dbt models, defining shared metrics, and answering questions about how we grow. Your background may be in analytics engineering, data science or data engineering. You enjoy building data models that others can trust and reuse, and working closely with business teams to put that data to use. You'll be based in our New York office, sitting with the Growth data team. Our go-to-market data is spread across a CRM, marketing automation and messaging platforms, sales engagement and call tools, support tooling, and our own product. Making those systems agree with each other, and with what customers actually do in Legora, is the heart of this job. We're an AI-first data team. We believe the data function should be redesigned around what AI now makes possible, not retrofitted with it, and we want someone excited to help define what that looks like in practice. We're a small, centralised team supporting the whole company. This seat is for Growth. Working closely with RevOps and the GTM teams, you'll help answer questions like: • Marketing: acquisition funnels, attribution and CAC, campaign measurement, and what actually moves enterprise legal buyers. • Sales & RevOps: what drives pipeline, sales performance and pilot-to-customer conversion. • Customer Success: account health, expansion and churn signals, and renewals in a category that didn't exist three years ago. • Lifecycle: getting modelled data back into the CRM and messaging tools, so teams act on it where they already work. You'll partner directly with leaders across Marketing, Sales, RevOps and Customer Success who are data-fluent and eager to act on the wealth of data across our business. Your role is to bring structure to that data, uncover insights together, and build the tools and foundations that let them explore questions for themselves, shaping how we sell, where we spend, how we price, and which customers we invest in. What you will be doing • Own the Growth data model: turn raw data from our CRM, marketing, messaging, sales engagement and support systems into trusted, documented dbt models that join cleanly to product usage. • Bring new GTM sources into the warehouse when you need them, and partner with data engineering on anything that needs to scale beyond your immediate use case. • Work with RevOps and GTM leaders on the metrics that matter (pipeline, conversion, attribution, CAC, retention and expansion), so each has one trusted definition across teams. • Together with RevOps and Marketing Ops, get modelled data back into the business systems, so Sales, Marketing and Customer Success can act on it in the tools they already use. • Flag the field and process gaps that make CRM data hard to trust, and help RevOps and Marketing Ops fix them at the source. • Run the deep dives that move the business: where pipeline leaks, which campaigns create revenue, what wins a pilot, which accounts are at risk or ready to grow. Proactively surface the questions nobody is asking yet. • Build dashboards and reporting that scale beyond you, so GTM teams can answer their own questions where possible. • Help shape how the data team operates as we scale: standards, tooling, ways of working. What you'll need • Strong proficiency in SQL and Python. • Genuine depth in data modelling and analytics engineering: dbt, dimensional modelling, testing, semantic layers, and the judgement to build models others trust and reuse. • Hands-on experience modelling data from growth-side business systems, for example: • CRM (Salesforce, HubSpot) • Marketing automation and messaging (Customer.io, Braze, Marketo, Iterable) • Sales engagement and conversation intelligence (Outreach, Salesloft, Gong) • Support and in-app messaging (Intercom, Zendesk) • You know these systems' quirks: overwritten fields, missing history, duplicate contacts, and opportunity stages that mean different things to different teams. • A solid grasp of GTM analytics: funnels, attribution, pipeline and forecasting, cohort retention, and net revenue retention. • Excellent communication skills and the confidence to influence decisions through data storytelling, including pushing back when the data doesn't support what someone wants to do. Nice to have • Experience in B2B SaaS with an enterprise, sales-led motion. • Hands-on experience with our stack: Snowflake, dbt, Hex, Dagster, dlt, Salesforce. • Experience with reverse ETL or data activation, whether a tool like Hightouch or Census or syncs you built yourself. • Experimentation or modelling on GTM questions: campaign holdouts, uplift, propensity or forecasting models. • Prior experience as an early data hire at a fast-growing company, i.e. you've built the muscle, not inherited it. Most importantly, you are someone who • "Gets stuff done" and understands building a $10bn company isn't always glamorous and takes hard work and long hours. • Someone who thrives in an onsite environment, this is where we get the most out of our day • Thrives in a fast-paced environment where the answers aren't always clear and processes are few. • Is genuinely domain-curious. You don't need to have sold to law firms before, but you should be excited to learn how they buy.