Senior Data Scientist (JP5860)

Ovations Technologies; Johannesburg; On-site; ZAR 1,000,000 - 1,400,000; Full time

Job summary

Ovations Technologies is recruiting a Senior Data Scientist for a 12-month contract in the banking sector. You will build and optimize end-to-end ML, AI, and decisioning solutions across Personal & Private Banking and Digital channels, leveraging the Databricks platform for feature engineering and model development.

The role focuses on predictive modelling, experimentation, and governance, with collaboration across PPB, Digital, Risk, and MLOps teams to deliver production-ready analytics assets.

Qualifications

• Degree in Data Science, Computer Science, Engineering, Mathematical Statistics, Actuarial Science, Econometrics, or a quantitative field.
• Hands-on Databricks experience (Notebooks, Workflows, MLflow, Feature Store/Delta tables).
• Strong proficiency in Python and SQL for large-scale data manipulation and machine learning.
• Domain modelling experience in financial services (customer propensity, next-best-action, retention) or risk models.
• End-to-end execution: feature engineering, model tuning, validation, governance, and productionization with engineering teams.

Responsibilities

• Model Development & GenAI: Design, train, and optimize ML/AI models (customer propensity, next-best-action, risk, fraud, and GenAI use cases) using Databricks and MLflow.
• Feature Engineering & Governance: Build and maintain reusable enterprise feature pipelines using Databricks Feature Store and Delta tables with full quality and lineage controls.
• Decisioning & Optimization: Develop decision science models to enhance customer acquisition, engagement, cross-sell strategies, and operational outcomes.
• Monitoring & Governance: Monitor model performance, stability, and drift; produce complete model documentation and governance artifacts for risk compliance.
• Stakeholder Collaboration: Partner with PPB, Digital, Risk, and MLOps teams to translate business needs into production-ready analytical solutions.

Skills

Python

SQL

Model Deployment

Education

Bachelor's or Master’s degree in Data Science / CS / Engineering / Statistics / Econometrics or related quantitative field

Tools

Databricks

MLflow

Feature Store

Delta Tables

Job description

Role Overview

We are looking for a Senior Data Scientist,for our client in the banking sector, for a 12-month contract to build and optimize end-to-end machine learning, AI, and decisioning solutions across Personal & Private Banking (PPB) and Digital channels. Leveraging the Databricks platform, you will focus on feature engineering, predictive modelling, experimentation, and decision science to deliver scalable analytical assets.

Key Responsibilities

• Model Development & GenAI: Design, train, and optimize ML/AI models (customer propensity, next-best-action, risk, fraud, and GenAI use cases) using Databricks and MLflow.

• Feature Engineering & Governance:Build and maintain reusable enterprise feature pipelines using Databricks Feature Store and Delta tables with full quality and lineage controls.

• Decisioning & Optimization: Develop decision science models to enhance customer acquisition, engagement, cross-sell strategies, and operational outcomes.

• Monitoring & Governance: Monitor model performance, stability, and drift; produce complete model documentation and governance artifacts for risk compliance.

• Stakeholder Collaboration: Partner with PPB, Digital, Risk, and MLOps teams to translate business needs into production-ready analytical solutions.

Minimum Requirements

• Education: Degree in Data Science, Computer Science, Engineering, Mathematical Statistics, Actuarial Science, Econometrics, or a quantitative field.

• Databricks Stack: Hands-on experience using Databricks (Notebooks, Workflows, MLflow, and Feature Store/Delta tables).

• Technical Skills: Strong proficiency in Pythonand SQL for large-scale data manipulation and machine learning.

• Domain Modelling: Proven experience building customer (propensity, next-best-action, retention) or operational/risk models in financial services.

• End-to-End Execution: Demonstrated track record of feature engineering, model tuning, validation, governance, and productionizing models alongside engineering teams.