Senior Data Scientist - Product

The role
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.

What you will be doing

• 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.

What you'll need

• Strong proficiency in SQL and Python.
• Solid grasp of data modelling and what it takes to build analytical work that is reliable and trusted.
• 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.
• Genuine depth in at least one of the following, with competence across the rest and curiosity to grow:
  
  • Data modelling and analytics engineering (dbt, dimensional modelling, semantic layers, self-service)
  • Experimentation and causal inference (A/B test design, quasi-experiments, statistical rigour)
  • Machine learning and applied data science (forecasting, prediction, segmentation, evaluation)
  • Product analytics and metric design (funnels, cohorts, adoption frameworks, North Star metrics)

Nice to have

• Experience in a product-led or SaaS environment, ideally B2B.
• Hands-on experience with our stack: Snowflake, dbt, Hex, Dagster.
• 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.
• 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 worked on every part of a business, but you should be excited to.