As a Senior Data Scientist for Product at Legora you will turn data into decisions. You'll sit close to the business, taking questions end-to-end: shaping the metric, modelling the data in dbt, running the analysis, and making the recommendation. You'll pull in new data sources when you need to. Insights are useful; impact is what we hire for
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
There's no single profile we hire for. Some of us are strongest at data modelling and analytics engineering, some at experimentation and causal inference, some at machine learning, some at stakeholder influence. You'll likely be excellent at one or two of these and competent across the rest. That's the bar
We're a small, centralised team supporting the whole company, hiring for the person, not the seat. Depending on your strengths and where we have the biggest gap when you join, you could be embedded primarily with
Product: instrumentation, feature adoption, user behaviour, A/B testing, shaping the roadmap with PMs and designers
Finance & RevOps: ARR, NRR, forecasting, board reporting, pricing analytics across a 40-country footprint, and unit economics for an AI-native product
Growth & Marketing: acquisition funnels, attribution, campaign measurement, lifecycle analytics, and what actually moves enterprise legal buyers
GTM & Customer Success: pipeline analytics, customer health, expansion signals, and retention drivers in a category that didn't exist three years ago
You'll partner directly with leaders across Product, Engineering, Finance, and GTM, most of whom are unusually data-fluent and will happily open a SQL editor with you. Your work will directly influence how we prioritise, how we sell, how we price, and how we build
Partner with stakeholders across Product, Finance, GTM, Growth, and beyond to translate ambiguous questions into structured analyses and clear recommendations
Define the metrics that matter, design the experiments or analyses that test them, and measure the impact of what we ship
Conduct deep-dive analyses on the questions that move the business, and proactively surface the questions nobody is asking yet
Model the data you need for your work in dbt, pulling in new sources when necessary, and partner closely with data engineering on anything that needs to scale beyond your immediate use case
Build dashboards and reporting that scale beyond you, so the company can answer its own questions where possible
Help shape how the data team operates as we scale: standards, tooling, ways of working