Data Scientist — Customer Support & Automation

Wise; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

Wise is hiring an experienced Data Scientist for its Customer Support team in London. You will analyse customer and operational data to measure service quality, diagnose issues and shape automation strategies.

The role emphasizes turning insights into concrete product and process improvements across human and automated channels. You will work with Product, Design, Operations and Engineering to turn complex journeys into actionable problems, applying ML, NLP and causal inference to improve

Qualifications

• Simplify complex systems into clear hypotheses and tractable questions.
• Strong analytical judgement to derive quick, useful answers and know when evidence is sufficient.
• Proficient SQL and Python with large, imperfect datasets.
• Solid stats background and causal inference with controlled experiments and observational studies.
• Experience applying ML, NLP and LLM-based techniques to analyze customer behaviour.

Responsibilities

• Measure and diagnose customer support quality across channels.
• Design metrics reflecting meaningful outcomes and cost effectiveness.
• Identify root causes using ML, statistics and domain expertise to drive outcomes.
• Turn insights into proposals for product changes, automation or better human support.
• Design experiments and analyse results to validate improvements.
• Shape the automation strategy and roadmap for long-term value.

Skills

SQL

Python

Statistical analysis

Causal inference

Machine Learning

NLP

LLM-based techniques

Experiment design

Product and business judgement

Tools

AWS

Job description

Wise is hiring an experienced Data Scientist for its Customer Support team in London. You will analyse customer and operational data to measure service quality, diagnose issues and shape automation strategies.

The role emphasizes turning insights into concrete product and process improvements across human and automated channels. You will work with Product, Design, Operations and Engineering to turn complex journeys into actionable problems, applying ML, NLP and causal inference to improve