data
Posted 1 weeks agoSenior Data Scientist
at Legora
On-site
Responsibilities
- - 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.
Requirements
- Our AI-native workspace lets legal professionals move faster, think more clearly, and operate with sharper precision.
- We’ve scaled to $100M+ in ARR, with teams across Europe, North America and APAC, and continue to expand through acquisitions including Qura, Walter AI and Graceview.
- 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.
- 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.
- 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.
- - Finance & RevOps: ARR, NRR, forecasting, board reporting, pricing analytics across a 40-country footprint, and unit economics for an AI-native product.
- - 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.
- 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.
- 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.
- - 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 -