Product Analytics Scientist for AI Platform

wordsmith; City of Edinburgh; On-site; GBP 70,000 - 110,000; Full time

Job summary

Wordsmith is seeking a Data Scientist (Product Analytics) to influence product strategy through rigorous measurement of user behavior and engagement. You will own the analytics framework across product, engineering, and design, turning data into actionable insights that guide roadmap decisions.

You’ll lead experimentation, build predictive models, and collaborate with cross-functional teams to maximize adoption and retention in a fast-growing enterprise SaaS environment.

Qualifications

• 3–5+ years in product data science or product analytics.
• Strong proficiency in SQL and Python/R for data manipulation and modeling.
• Genuinely product-minded with curiosity about user behavior and outcomes.
• Rigorous experimentation background with design and analysis of A/B tests.
• Exceptional communication skills to translate data findings for non-technical audiences.
• Thrives in ambiguity and can navigate unstructured problems.
• Bias toward action with pragmatic startup execution.

Responsibilities

• Design, instrument, and validate product analytics including event tracking, funnels, retention curves, and feature adoption metrics.
• Build and maintain the core metrics framework and dashboards for the company.
• Partner with engineering to improve data instrumentation and data quality at the source.
• Develop intuitive self-serve analytics tooling and data models for internal stakeholders.

Skills

Product analytics

SQL

Python

R

Experimentation

Data storytelling

Communication

Ambiguity tolerance

Bias toward action

Tools

SQL

Python

R

Job description

Wordsmith is seeking a Data Scientist (Product Analytics) to influence product strategy through rigorous measurement of user behavior and engagement. You will own the analytics framework across product, engineering, and design, turning data into actionable insights that guide roadmap decisions.

You’ll lead experimentation, build predictive models, and collaborate with cross-functional teams to maximize adoption and retention in a fast-growing enterprise SaaS environment.