Senior Data Scientist

Praesignis (Pty) Ltd; Gauteng; On-site; ZAR 900,000 - 1,500,000; Full time

Job summary

Praesignis (Pty) Ltd in the banking sector seeks a Senior Data Scientist to design, develop and optimise machine learning, AI and decisioning solutions for Personal and Private Banking and Digital channels.

You will partner with PPB, Digital, Product and Risk teams, build production-ready models on Databricks using MLflow, and mentor junior data scientists to raise modelling standards across the business.

Qualifications

• Degree in a quantitative field (e.g., CS, Math, Stats)
• Masters or PhD preferred for advanced modelling
• Experience with large datasets and governance practices

Responsibilities

• Identify high-value analytical opportunities with PPB, Digital, Product and Risk teams
• Design, train and optimise ML models using Databricks MLflow
• Develop customer propensity, next best action, and retention models
• Build reusable feature pipelines and feature governance
• Produce model documentation and governance artefacts
• Mentor junior Data Scientists and contribute to standards

Skills

Databricks notebooks

Databricks feature store

Python

SQL

Machine learning

Statistical modelling

Generative AI

Data visualization

Stakeholder management

Experimentation

Education

Masters in quantitative field

Doctorate (advantage)

Tools

Databricks

MLflow

Feature Store

Job description

Our client in the banking industry is looking for a Senior Data Scientistto 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.

Key Responsbilities

• 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 reusable feature pipelines using Databricks Feature Engineering and Delta tables.
• Define, create and govern reusable enterprise features for inclusion within the Databricks Enterprise Feature Store.
• Establish feature definitions, feature quality controls, lineage and monitoring standards across business domains.
• Perform feature engineering, feature selection and feature importance analysis to improve model performance and reuse.
• Develop optimisation and decisioning models that support customer engagement, product recommendation and operational decision 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 and recommend
  improvements where required.
• Develop Generative AI use cases where appropriate, including customer support,
  document intelligence and knowledge-based assistants.
• Work closely with ML Engineers to productionise models and features into
  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.

Core Deliverables

• Production-ready machine learning models supporting PPB and Digital use cases.
• Reusable business features deployed within the Databricks Enterprise Feature Store.
• Customer decisioning models supporting acquisition, retention, engagement and cross-sell strategies.
• Risk and operational models supporting improved decision quality and business performance.
• Feature libraries, model documentation and governance artefacts.
• Analytical insights and recommendations delivered to business stakeholders.
• Measurable business value from deployed machine 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
• Feature Engineering
• 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 Doctorate will be an added advantage.

Preferred Certifications

• 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

Technical/Professional Knowledge

• Strong experience developing machine learning models on Databricks.
• Strong understanding of feature engineering, feature selection and feature optimisation techniques.
• Experience building and governing reusable enterprise features within Feature Stores.
• Experience using Databricks MLflow for model tracking, experimentation and model governance.
• Experience developing customer propensity, risk, fraud, retention and optimisation models.
• Strong knowledge of statistics, machine learning 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, prompt engineering and applied AI use cases.
• Ability to translate business problems into analytical solutions and measurable business outcomes.
• Strong stakeholder engagement, communication and business consulting skills.
• Experience delivering end-to-end data science use cases from concept through production deployment.
• Self-driven and able to operate effectively in a fast-paced, outcome-focused environment.