Finance Data Scientist: ML, MLOps & Actuarial Analytics

Centrax Digital UK; Johannesburg; On-site; ZAR 800,000 - 1,500,000; Full time

Job summary

Centrax Digital UK is looking for a Data Scientist to apply advanced analytics, statistical modelling and machine learning to finance and actuarial data. You will translate business questions into analytical problems, build and productionise models, and turn results into insight that shapes decision-making across the finance operating model.

Key focus areas include problem framing, model development and MLOps deployment, analytical data products, storytelling with visuals, and governance

Qualifications

• 6+ years in data science or quantitative modelling with 3+ years in insurance or financial services.
• Advanced Python and/or R, plus strong SQL for large-scale data manipulation.
• Practical ML with scikit-learn, XGBoost, TensorFlow or PyTorch.
• Statistics expertise: regression/GLMs, time-series, hypothesis testing, experimental design.
• Production deployment on Azure ML, Databricks or SageMaker with MLOps tooling.
• Strong finance data flows, transformation and cleansing; Informatica or similar ETL.
• Familiarity with Data Mesh, MDM and finance data lakes/warehouses.
• Visualization and storytelling in Power BI, Tableau or equivalent.
• Understanding of finance and actuarial data, accounting principles and reporting standards.
• Git, Jira; experience with Agile and Waterfall.

Skills

Python/R

SQL

ML frameworks

Statistics

Model deployment

ETL tools

Data Mesh/MDM

Visualization tools

Finance/Actuarial data

Git/Jira/Agile

Tools

Azure ML

Databricks

SageMaker

Informatica

Job description

Centrax Digital UK is looking for a Data Scientist to apply advanced analytics, statistical modelling and machine learning to finance and actuarial data. You will translate business questions into analytical problems, build and productionise models, and turn results into insight that shapes decision-making across the finance operating model.

Key focus areas include problem framing, model development and MLOps deployment, analytical data products, storytelling with visuals, and governance