Senior Product Data Scientist - Text Analytics

Harnham
London, UK
about 1 month ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
£110,000.0 - £125,000.0
Working hours
Shift work
Job source

Tech stack

Python (Programming Language) Natural Language Processing SQL Databases Text Analysis

Requirements

Stack: Python, SQL, NLP, Text Analytics, Semantic Analysis, Experimentation, dbt

Benefits & conditions

Build and own AI product evaluation frameworks and success metrics

Use NLP, semantic analysis and behavioural analytics to identify opportunities for improvement

Design and analyse A/B tests and experimentation programmes

Partner with Product Managers, Engineers and Directors to influence roadmap decisions

Link AI feature usage to activation, retention, engagement and business outcomes

Mentor analysts or data scientists and raise analytical standards across the team

Key Details

Salary: £110,000-£125,000 (Senior/Tech Lead) or £120,000-£145,000 (Staff)

RSUs worth 50-100% of base salary

Potential £10,000 sign-on bonus

Hybrid London (ideally 2-3 days per week, flexible to 1 day)

About the company

Do you want to shape how one of Europe’s leading AI products measures success?

Have you worked with large-scale text, conversational or semantic datasets to uncover product insights?

Are you ready to influence product strategy through data rather than just reporting on it?

We’re hiring for a well-funded AI-native scale-up building one of the world’s leading AI video generation platforms. Their technology enables users to create avatar-led videos from text and is used globally across training, internal communications, sales enablement and marketing. Having achieved significant growth and enterprise adoption, they are investing heavily in understanding how users interact with AI products and how those experiences can be improved.

This role sits at the intersection of Product, Data Science and AI. You’ll analyse large-scale conversational datasets, build evaluation frameworks for AI-native features, and directly influence product decisions through experimentation and insight generation.

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