Data Scientist; Product Analytics

Data Scientist (Product Analytics) Edinburgh or London Wordsmith

Wordsmith is building the legal operations platform for in‑house teams, and we are growing fast.

We believe in‑house legal is becoming one of the most important functions in the AI era. Every business is moving faster, but legal work is still too often scattered across inboxes, chats, documents, and disconnected systems.

Wordsmith brings that work into one system. Requests come in from across the business, AI agents handle the routine, lawyers stay in control where judgment matters, and every step is captured as work happens.

We are built for in‑house legal, not law firms. Our goal is to help legal teams move faster, reduce unnecessary external counsel spend, and become a function the business can run with.

More than 500 companies, including Financial Times, Safelite, , Canva, Starling, and Sage, use Wordsmith. Backed by $100M from Index Ventures, General Catalyst, Highland Europe, and others, we are building one of the defining companies in legal AI.

As our product scales across global enterprises, understanding exactly how users interact with our AI agents is critical to our success. This role exists to turn raw usage data into the strategic insights that will shape the future of our product roadmap.

You will sit directly at the intersection of Product, Engineering, and Design, acting as the analytical conscience of the team. This is a high‑impact role requiring a blend of deep technical skill and product intuition. You will define what "good" looks like for our platform and build the metrics, models, and experiments to track it.

This isn't a passive reporting or basic data‑pull role. It combines advanced product analytics, experimental design, and strategic decision support. Your mission is to uncover the behavioural patterns that drive core adoption, optimise user retention, and ensure our AI agents deliver maximum value to every customer.

Product Analytics & Metrics Framework

• Instrument Core Analytics: Design, instrument, and validate product analytics—including event tracking, funnels, retention curves, and feature‑adoption metrics.

• Build the Metrics Infrastructure: Build and maintain the core metrics framework and the dashboards that surface them to the broader company.

• Improve Data Quality: Partner with engineering to improve data instrumentation, event schemas, and tracking quality at the source.

• Empower Self‑Serve Access: Build intuitive self‑serve analytics tooling and data models so internal stakeholders can easily answer their own day‑to‑day questions.

Experimentation & Deep‑Dive Analysis

• Own Statistical Rigour: Design, run, and assess A/B tests and other controlled experiments; own the statistical validity of our testing culture from hypothesis definition to the final decision.

• Analyze User Behavior: Conduct deep‑dive behavioural analyses, cohort performance tracking, and churn driver investigations to uncover critical product friction points.

• Develop Predictive Models: Build predictive and segmentation models to anticipate user needs, flag churn risk, and identify expansion opportunities.

Cross‑Functional Collaboration

• Translate Ambiguity: Translate ambiguous or open‑ended product questions into well‑scoped technical analyses and clear, actionable recommendations.

• Advocate for the User: Act as the voice of user behaviour within the GTM and Product teams, feeding builders with empirical insight on how users actually interact with the platform.

Essential

• 3–5+ years of experience in a dedicated product data science or product analytics role.

• Strong technical toolkit: Advanced proficiency in SQL and Python/R for data manipulation, statistical analysis, and predictive modelling.

• Genuinely product‑minded: Deeply curious about why users behave the way they do, not just what the top‑line numbers say.

• Rigorous experimentation background: Hands‑on experience designing and analysing A/B tests with a firm grasp of experimental statistics.

• Exceptional communication skills: Able to translate complex data findings crisply into clear stories for non‑technical audiences, influencing product direction without…