Data Scientist (Contract)

Job description

We're looking for an intermediate Data Scientist to join our Data Science team on a 12-month contract. The team works across established and emerging data products and analytical initiatives within Experian Marketing Services. Current priorities include the Mosaic refresh, Audience Taxonomy and Spend Analytics, alongside new machine-learning and AI opportunities across our consumer and commercial businesses. This is a hands-on role where you'll apply statistical analysis, experimentation and machine learning to real business and product problems.

Working within a multidisciplinary scrum team, you'll take analytical work from problem definition and data exploration through modelling and validation, collaborating with Product, Data Engineering and other stakeholders to turn data science into practical outcomes. You will report to Data Science and Engineering Manager What you'll be doing: Analyse complex datasets to identify patterns and insights that inform product and business decisions.

Develop and evaluate statistical and machine-learning models, including data preparation, feature engineering, model selection, validation and performance monitoring. Design and analyse experiments using methods such as regression analysis, hypothesis testing and A/B testing. Contribute to data science and AI initiatives, including Mosaic, Audience Taxonomy, Spend Analytics, embeddings, retrieval-augmented generation (RAG) and prompt engineering, where relevant.

Collaborate with scrum teams and stakeholders to translate requirements into reliable analytical solutions, document methodologies and communicate findings clearly to technical and non-technical audiences.

Qualifications

What we're Looking

5 years of working experience applying data science and machine-learning techniques to business or product challenges, ideally within marketing analytics, customer segmentation, audience analytics or a related customer-data environment. Proficiency in Python and SQL for analysing large and complex datasets, using libraries such as pandas, scikit-learn or similar tools. Strong knowledge of statistics, experimentation and machine learning, including regression, hypothesis testing, experimental design, feature engineering, model selection and evaluation.

Experience working with modern cloud data platforms, such as Snowflake, and the ability to structure and solve complex or ambiguous problems. Clear communication, planning and collaboration skills, with the ability to work independently and partner effectively with Data Science, Engineering, Product and business teams across locations, time zones and changing priorities.

Additional Experience

That Would Be Valuable: Experience with generative AI and large language model applications, including embeddings, retrieval-augmented generation (RAG), vector search or prompt engineering. Knowledge of deep-learning techniques or experience working with large-scale datasets. Experience developing analytical solutions into repeatable products, capabilities or business processes. Exposure to model validation, monitoring and ongoing performance evaluation.or