Senior Data Scientist

The Senior Data Scientist is responsible for designing, developing, and optimising machine learning, artificial intelligence (AI), and decisioning solutions that drive customer, risk. The role applies advanced analytics, machine learning, and AI techniques to solve complex business problems, enhance customer experiences, and support intelligent decision-making. Leveraging the Databricks platform, the successful candidate will develop scalable analytical assets, reusable features, and high-impact predictive models that deliver measurable business value., * 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., * Opportunity to work on innovative AI, machine learning, and advanced analytics initiatives.
• Exposure to cutting-edge technologies including Databricks, MLflow, Generative AI, and Feature Store platforms.
• Collaborative environment working alongside business, digital, risk, and technology teams.
• Opportunities for professional growth, mentorship, and technical leadership.
• Ability to deliver measurable business value through data-driven innovation and intelligent decisioning.

Essential Skills & Experience

• 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.

Desirable Skills & Experience

• Experience with Generative AI, Large Language Models (LLMs), and prompt engineering.
• Exposure to customer decisioning and optimisation platforms.
• Experience delivering end-to-end data science solutions from concept through production deployment.
• Experience in banking, financial services, digital channels, or customer analytics environments.
• Experience with cloud-based AI and machine learning platforms.
• Advanced qualifications in quantitative disciplines., * Degree in Computer Science, Data Science, Engineering, Mathematical Statistics, Actuarial Science, Mathematics, Econometrics, or a related quantitative field.
• Master’s Degree or PhD will be advantageous.
• Relevant professional certifications are advantageous, including:
• Databricks Machine Learning Engineer Certification
• Databricks Data Engineer Certification
• Microsoft Azure AI Certifications
• SAS Data Scientist Certifications
• AWS or Google Cloud AI/ML Certifications
• Machine Learning, Artificial Intelligence, or Data Science certifications from recognised providers such as Microsoft, Databricks, SAS, Coursera, or DeepLearning.AI.

Behavioural Competencies

• Strong analytical thinking and problem-solving abilities.
• Excellent stakeholder engagement and relationship management skills.
• Strong written, verbal, and presentation communication skills.
• Ability to work collaboratively across business, technology, and data teams.
• Self-motivated and capable of working independently.
• Results-oriented with strong commercial awareness.
• Adaptable and comfortable operating in a fast-paced environment.
• Commitment to continuous learning, innovation, and knowledge sharing.