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.
What We're Looking For:
- At least five years of experience applying data science and machine-learning techniques to business or product challenges, within marketing analytics, customer segmentation, audience analytics or a related customer-data environment.
- Proficiency in Python and SQL for analyzing datasets, using libraries such as pandas, scikit-learn or similar tools, with experience working on modern cloud data platforms such as Snowflake.
- Knowledge of statistics, experimentation and machine learning, including regression, hypothesis testing, experimental design, feature engineering, model selection and evaluation. Experience with deep-learning techniques or large-scale datasets would be valuable.
- Experience structuring and solving complex or ambiguous problems and translate analytical solutions into repeatable products, capabilities or business processes, including model validation, monitoring and ongoing performance evaluation.
- Experience managing assigned work and collaborating with Data Science, Engineering, Product and business teams across locations and time zones, including coordinating deliverables and priorities. Familiarity with generative AI and large language model applications, such as embeddings, retrieval-augmented generation (RAG), vector search or prompt engineering, is preferred.
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Benefits and Perks:
- Hybrid work arrangement: You will divide your time between working remotely and working from the office. You will work from the office at least two days per week. You and your manager will agree on your office days, taking business and client needs into consideration.
- Inclusive paid leave
- People-first and inclusive workplace
- Career growth opportunities
- Exposure to diverse, global teams
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