Senior Data Scientist

What you will be doing:

• Design, develop, train, and optimise Machine Learning models using Databricks, MLflow, and distributed computing environments.
• Develop customer propensity, next-best-action, next-best-product, customer value, retention, and engagement models.
• Develop risk, collections, fraud, and operational models to improve business performance and decision-making.
• Build and maintain reusable feature pipelines using Databricks Feature Engineering and Delta tables.
• Define, develop, and govern reusable enterprise features for inclusion in the Databricks Enterprise Feature Store.
• Establish feature definitions, quality controls, lineage, and monitoring standards across business domains.
• Perform feature engineering, feature selection, and feature importance analysis to improve model performance and reusability.
• Develop optimisation and decisioning models to support customer engagement, product recommendations, and operational strategies.
• Conduct exploratory data analysis, statistical analysis, hypothesis testing, and model validation using Databricks notebooks and workflows.
• Perform model tuning, calibration, challenger model development, and performance benchmarking.
• Monitor model performance, stability, drift, and business outcomes, recommending improvements where required.
• Develop Generative AI use cases where appropriate, including customer support, document intelligence, and knowledge-based assistants.
• Collaborate with Machine Learning Engineers to productionise models and features within enterprise platforms and decisioning systems.
• Produce model documentation, validation reports, and governance artefacts in line with model risk management requirements.
• Translate business challenges into analytical solutions and actionable recommendations for stakeholders.
• Mentor junior Data Scientists and contribute to modelling standards, reusable frameworks, and analytical best practices.

What we are looking for:

• A qualification in Computer Science, Data Science, Engineering, Mathematical Statistics, Actuarial Science, Mathematics, Econometrics, or a related quantitative field.
• A Master’s degree or Doctorate will be advantageous.
• Strong experience developing and deploying Machine Learning models using Databricks.
• Minimum 4 years’ experience in Data Science, Machine Learning, or a related quantitative field, with hands-on experience developing and deploying Machine Learning models.
• Strong understanding of feature engineering, feature selection, and feature optimisation techniques.
• Experience building and governing reusable enterprise features within Feature Stores.
• Hands-on experience with Databricks Notebooks, Workflows, MLflow, Delta tables, and Databricks Feature Engineering.
• Experience using MLflow for model tracking, experimentation, and model governance.
• Experience developing customer propensity, risk, fraud, retention, and optimisation models.
• Strong knowledge of statistics, Machine Learning, predictive and prescriptive analytics, and decision science methodologies.
• Experience working with large-scale customer, behavioural, transactional, and digital datasets.
• Experience designing model monitoring and performance measurement frameworks.
• Knowledge of Generative AI, Large Language Models (LLMs), prompt engineering, and applied AI use cases.
• Experience with experimentation, hypothesis testing, model validation, tuning, calibration, and performance benchmarking.

Please note that if you do not hear from us within 3 weeks, consider your application unsuccessful.

Please note that most of our positions are remote; however, candidates should reside within travelling distance of the client site, as the working arrangements may change depending on business requirements.