Senior Data Scientist
Overview
The Senior Data Scientist is responsible for designing, developing, and optimising machine learning, artificial intelligence (AI), and decisioning solutions that drive customer, risk, and operational outcomes across Personal and Private Banking (PPB) and Digital Channels. The role applies advanced analytics, machine learning, and AI techniques to solve complex business problems, enhance customer experiences, and support intelligent decision-making.
Requirements
- Strong experience developing machine learning models using Databricks and MLflow.
- Advanced proficiency in Python and SQL.
- Strong understanding of machine learning, statistical modelling, predictive analytics, and prescriptive analytics.
- Experience with feature engineering, feature selection, and feature optimisation techniques.
- Experience building and governing reusable enterprise features within Feature Stores.
- Experience developing customer propensity, fraud, risk, retention, and optimisation models.
- Experience using Databricks Feature Engineering and Feature Store capabilities.
- Strong knowledge of statistics, machine learning methodologies, and decision science techniques.
- Experience working with large-scale customer, behavioural, transactional, and digital datasets.
- Experience designing model monitoring and performance measurement frameworks.
- Ability to translate business problems into analytical solutions and measurable business outcomes.
- Strong stakeholder engagement, communication, and business consulting skills.
Responsibilities
- Partner with PPB, Digital, Product, and Risk teams to identify and prioritise high-value analytical opportunities.
- 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 quality.
- Build and maintain reusable feature pipelines using Databricks Feature Engineering and Delta Tables.
- Create and govern reusable enterprise features within the Databricks Enterprise Feature Store.
- Conduct exploratory data analysis, statistical modelling, hypothesis testing, model validation, and performance benchmarking.
- Develop optimisation and decisioning models that support customer engagement, product recommendations, and operational strategies.
- Monitor model performance, stability, drift, and business outcomes, implementing improvements where required.
- Develop Generative AI use cases including customer support solutions, document intelligence, and knowledge-based assistants.
- Collaborate with ML Engineers to productionise analytical solutions and decisioning models.
- Produce model documentation, validation reports, and governance artefacts aligned to model risk management requirements.
- Mentor junior Data Scientists and contribute to analytical standards and best practices.
How to apply
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