Product Performance Lead

Job Purpose

As Product Performance Lead, you will be a senior, hands-on analyst working with Product Owners to answer commercial questions on revenue, margin, pricing and customer behaviour.

You will work at the forefront of AI-assisted analytics, using large language models (LLMs), AI coding assistants and agents every day to explore data, write and test code, and automate repeat work. You will know where these tools fail and check their output before anyone relies on it. This is an individual contributor role part of the Product Data team, led by the Chief Product Officer.

Key Responsibilities

Data Extraction & Quality

• Turn Product Owners’ questions into clear analytical requirements.
• Write efficient SQL and Python to extract, join and clean data from databases, APIs and files.
• Reconcile sources, test outputs, and resolve or escalate data issues before results are shared.
• Keep work reproducible with version control, documentation, tests and a clear audit trail.
• Work with central data engineering on pipelines and shared data and recommend data quality improvements.
  
  
  

Commercial Performance Analysis

• Track product, service and channel performance against commercial targets, and explain the root causes when results move.
• Find revenue drivers, monetisation opportunities and revenue leakage, and recommend specific corrective actions.
• Set KPIs, success criteria and baselines for launches, then design and evaluate A/B tests, trials and rollouts.
• Build business cases, forecasts, and revenue models with clear assumptions.
• Prepare regular product performance reviews for Product leadership that explain what changed and why.
  
  
  

AI-Assisted Analytics & Automation

• Use LLMs, AI coding assistants and agents as a core part of your daily work.
• Build repeatable AI workflows that connect data, tools and reporting through APIs, so routine analysis runs with little manual effort.
• Use LLMs to classify and summarise unstructured data, such as customer feedback, cancellation reasons and support notes, at scale.
• Validate AI output against source data, and document the checks, the limitations and the tasks where AI should not be used.
• Share prompts, workflows and lessons with the Product Data Team, and keep up with new models and tools.
  
  
  

Stakeholder Partnership

• Operate with high autonomy, proactively seeking stakeholder context, unblocking dependencies and using remote and in-office time effectively for deep analysis and structured documentation.
• Partner with Product, Marketing, Operations and Commercial teams to frame ambiguous questions and agree decision criteria.
• Prioritise work by commercial impact and work independently in a hybrid team.
• Present clear recommendations in short narratives and visuals, stating uncertainty and trade-offs.
  
  
  

What Success Looks Like in the First 12 Months

• Recurring reports and analyses run as AI-assisted workflows that the wider team reuses.
• Your recommendations have changed pricing, product or channel decisions, with measured results.
• Known data quality issues are documented, and the biggest ones are fixed or have an owner.
• Customer feedback and cancellation reasons are analysed with LLMs and reported on a regular cycle.
  
  
  

Essential Skills & Experience

• A track record of independent commercial analysis, from raw data to recommendations that changed decisions.
• Advanced SQL, including common table expressions (CTEs), window functions and query optimisation.
• Strong Python skills for data extraction, cleaning, analysis and API work.
• Sound statistical judgement, including method choice, experiment design, uncertainty and correlation versus causation.
• Daily, hands-on use of LLMs and AI coding tools (Claude, Copilot) in real analytical work, with examples of workflows you built and the time or quality gains they produced.
• A working understanding of how LLMs behave, including context limits, hallucination, prompt and context design, and when output needs checking.
• The ability to read, debug and test AI-generated code, and to spot when it is wrong.
• Advanced Excel and Power BI skills.
• Clear written and verbal communication with senior, non-technical stakeholders.