Al Senior Data Scientist

Indsafri; Johannesburg; On-site; ZAR 900,000 - 1,400,000; Full time

Job summary

Indsafri is seeking a senior data scientist to design and deploy machine learning, AI and decisioning solutions in PPB and Digital channels in South Africa. You will work on feature engineering, model development and governance using Databricks.

You will partner with PPB, Digital, Product and Risk teams to identify high-value opportunities, build reusable feature pipelines, monitor model quality and explore Generative AI use cases.

Qualifications

• Masters or Doctorate in a quantitative field.
• Strong experience on Databricks platform.
• Proficiency in Python and SQL.
• Experience with feature engineering and feature stores.
• Knowledge of Generative AI use cases.

Responsibilities

• Partner with PPB, Digital, Product and Risk teams to identify opportunities.
• Design, develop, train and optimise ML models using Databricks, MLflow and distributed compute environments.
• Develop customer propensity, next best action, next best product, customer value, retention and engagement models.
• Build and maintain reusable feature pipelines and govern enterprise features.
• Monitor model performance and conduct validation and governance artefacts.

Skills

Python

SQL

Machine Learning

Statistical Modelling

Feature Engineering

Experimentation

Generative AI

Stakeholder Management

Data Visualization

Model Validation

Education

Masters or Doctorate in quantitative field

Tools

Databricks Notebooks

Databricks MLflow

Databricks Feature Store

Delta Lake

Job description

To design, develop and optimise machine learning, AI and decisioning solutions that drive customer, risk and operational outcomes across Personal and Private Banking (PPB) and Digital channels.

Purpose

Apply advanced analytics, machine learning and AI techniques to solve business problems, improve customer experiences and support intelligent decisioning. The role focuses on feature engineering, model development, experimentation and decision sciencewhile leveraging the Databricks platformto deliver scalable and reusable analytical assets.

Key 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 compute 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 reusablefeature pipelines using Databricks FeatureEngineering and Delta tables.
• Define, create and governreusable enterprise featuresfor inclusion withinthe Databricks Enterprise Feature Store.
• Establish feature definitions, feature quality controls, lineage and monitoring standards across business domains.
• Perform feature engineering, featureselection and featureimportance analysis to improve model performance and reuse.
• Develop optimisation and decisioning models that supportcustomer engagement, product recommendation and operational decision strategies.
• Conduct exploratory data analysis, statistical analysis, hypothesis testingand model validation using Databricks notebooks and workflows.
• Perform model tuning,calibration, challenger model development and performance benchmarking.
• Monitor model performance, stability, drift and businessoutcomes and recommendimprovements where required.
• Develop Generative AI use cases where appropriate, including customer support, document intelligence and knowledge-based assistants.
• Work closelywith ML Engineers to productionise models and featuresinto enterprise platforms and decisioning systems.
• Produce model documentation, validation reports and governance artefacts in line with model risk management requirements.
• Mentor junior Data Scientists and contribute to modelling standards, reusable frameworks and analytical best practices.
• Production-ready machine learningmodels supporting PPB and Digitaluse cases.
• Reusable business featuresdeployed within the Databricks Enterprise Feature Store.
• Customer decisioning models supporting acquisition, retention, engagement and cross-sell strategies.
• Risk and operational models supporting improveddecision quality and business performance.
• Feature libraries, modeldocumentation and governance artefacts.
• Analytical insights and recommendations delivered to business stakeholders.
• Measurable business value from deployedmachine learning and AI solutions.

Key Skills

• Databricks Notebooks, Workflows and MLflow
• Databricks Feature Engineering and Feature Store
• Python and SQL
• Machine Learning and Statistical Modelling
• Predictive and Prescriptive Analytics
• Customer Decisioning and Optimisation
• Experimentation and Model Validation
• Generative AI and LLMs
• Data Visualisation and Storytelling
• Stakeholder Management

Qualifications

Computer Science, Data Science, Engineering, Mathematical Statistics, Actuarial Science, Mathematics, Econometrics or a related quantitative field.

Masters or Doctoratewill be an added advantage.

Preferred Certifications

• SAS Data ScientistCertifications
• AWS or Google Cloud AI/ML Certifications
• Machine Learning, Artificial Intelligence or Data Sciencecertifications from recognised providers such as Microsoft, Databricks, SAS, Coursera or DeepLearning.AI

Technical / Professional Knowledge

• Strong experience developing machine learning modelson Databricks.
• Strong understanding of feature engineering, feature selection and feature optimisation techniques.
• Experience building and governing reusableenterprise features within Feature Stores.
• Experience using Databricks MLflow for model tracking, experimentation and model governance.
• Experience developing customerpropensity, risk, fraud, retention and optimisation models.
• Strong knowledge of statistics, machinelearning and decisionscience methodologies.
• Experience working with large-scale customer, behavioural, transactional and digital datasets.
• Experience designing model monitoring and performance measurement frameworks.
• Knowledge of Generative AI, prompt engineering and applied AI use cases.
• Ability to translatebusiness problems into analytical solutionsand measurable business outcomes.
• Strong stakeholder engagement, communication and businessconsulting skills.
• Experience delivering end-to-end data science use cases from concept through production deployment.
• Self-driven and able to operateeffectively in a fast-paced, outcome-focused environment.