Partner with Product, Commercial, Marketing and other stakeholders to identify high-value opportunities, shape ambiguous problems and determine whether Data Science is the right intervention.

Independently select, build and improve modelling approaches, using feature engineering, tuning and appropriate algorithmic choices to improve performance.

Design robust offline evaluation strategies, selecting metrics that reflect problem-specific behaviour and trade-offs rather than relying solely on generic model-performance measures.

Design and support online experiments to evaluate real-world impact, working with Product and Analytics partners to define success metrics, guardrails and appropriate interpretation of results.

Own outcomes beyond model delivery: follow solutions through production and experimentation, determine whether they are creating the intended customer or commercial impact and drive iteration where they are not.

Design components of machine learning systems, such as feature-generation pipelines, model-scoring logic and inference workflows, working closely with Engineering to integrate solutions into production.

Collaborate with MLOps to deploy models and ensure appropriate monitoring, retraining and operational processes are in place, addressing issues such as drift, data-quality problems and performance degradation.

Write high-quality, tested and maintainable Python and SQL, contributing robust and reproducible solutions to shared production codebases.

Build practical AI-powered features where appropriate, such as solutions using prompts, embeddings or other generative AI capabilities, and evaluate their outputs systematically.

Use AI-assisted development tools to improve coding, analysis, experimentation and documentation, critically evaluating outputs and identifying opportunities to improve team workflows.

Communicate technical decisions, model behaviour, trade-offs and recommendations clearly, using evidence to influence product and business decisions and prioritisation.

Contribute to the wider Data Science capability through informal mentorship, peer review, knowledge sharing, reusable tooling and improvements to technical practices and ways of working.

Strong practical understanding of supervised machine learning, feature engineering, model selection, tuning, validation and evaluation, with experience independently improving model performance.

Strong Python and SQL skills, with experience developing robust Data Science solutions in shared production codebases.

Ability to take ambiguous customer or business problems, determine an appropriate Data Science approach and independently drive the work through to a usable solution and understood outcome.

Strong experience designing offline evaluation approaches and selecting metrics that appropriately reflect model performance and problem-specific trade-offs.

Experience designing, analysing and interpreting online experiments, with the ability to connect technical model performance to customer and commercial outcomes.

Experience deploying or contributing significantly to the deployment of machine learning models, with a good understanding of monitoring, retraining, data quality, model drift and common production issues.

Experience designing components of machine learning systems, such as feature pipelines, scoring logic or model workflows, and working effectively with Engineering to integrate them into production.

Strong understanding of testing, version control, reproducibility and maintainable software-development practices within a Data Science environment.

Able to make sound technical decisions independently and clearly articulate the trade-offs between alternative modelling, evaluation and implementation approaches.

Able to communicate complex technical concepts, assumptions and recommendations clearly and use evidence to influence Product, Engineering and business stakeholders.

Effective use of AI-assisted development tools to improve coding, analysis and experimentation, combined with strong judgement around validation, privacy, security and responsible use.

Strong awareness of data quality, privacy, fairness, security and customer-experience considerations when designing and deploying machine learning solutions.

Experience developing recommendation, ranking or personalisation systems would be beneficial.

Experience with customer modelling approaches such as propensity, uplift or customer lifetime value modelling would be beneficial.

Experience applying LLMs, embeddings or other generative AI techniques to build practical product or Data Science features would be useful.

Experience working in a B2C e-commerce, retail or other high-volume digital product environment would be useful.

Experience working with cloud-based machine learning infrastructure and services, particularly AWS, would be beneficial.

Familiarity with analytics engineering tooling such as dbt would be useful.

Experience with large-scale or near-real-time ML systems would be beneficial.

Experience mentoring junior team members, supporting peer review, sharing knowledge and acting as a key source of technical support would be beneficial.

A degree in Statistics, Mathematics, Economics, Computer Science or another relevant quantitative discipline can be helpful, but equivalent practical experience is equally welcome.

AWS for cloud-based machine learning infrastructure and services.

ML systems spanning feature-generation pipelines, model-scoring logic and inference workflows.

Production ML practices covering deployment, monitoring, retraining, data quality and model drift.

Offline evaluation and online experimentation to connect model performance with customer and commercial outcomes.

Generative AI capabilities including prompts, embeddings and other practical AI approaches.

AI-assisted development tools across coding, analysis, experimentation and documentation.

dbt within our wider analytics engineering tooling.