Job Overview

This role demands a hands-on technical leader capable of mining vast, unstructured financial datasets to build production-grade machine learning solutions. You will own the full data lifecycle—from initial mining and feature engineering to model training, validation, and deployment. Beyond coding, you will set the standard for statistical rigor within the team, ensuring our models remain accurate, explainable, and compliant with financial regulations. You will apply advanced statistical techniques to tackle our toughest challenges in personalisation and default prediction, understanding our customers among others.

Qualifications

Type of Qualification: First Degree

Field of Study: Degree in Information Technology, Computer Science, Actuarial Science, Statistics, Mathematics, Economics or any other related field.

Experience Required

Data & Analytics

4-7 years

Experience in working with unstructured data (e.g. Streams, images) Understanding of data flows, data architecture, ETL and processing of structured and unstructured data. Using data mining to discover new patterns from large datasets. Implement standard and proprietary algorithms for handling and processing data. Experience with common data science toolkits, such as SAS, R, SPSS, etc.

Experience with data visualisation tools, such as Power BI, Tableau, etc.

Proficiency in application of Structured and Unstructured Query languages e.g. SQL, Python, Power Query; QlikView; Tableau; R.

Proven understanding of financial services data processes, systems, and products. Experience in technical business intelligence. Knowledge of IT infrastructure and data principles. Project management experience.

Experience in building predictive models (credit scoring, propensity models, churn prediction, product recommendation, etc.)

Candidates must demonstrate a strong and successful track record of leading high-performing data analytics teams, driving impactful business outcomes through advanced quantitative analysis and statistical modelling.

Experience managing stakeholders translating technical concepts for business heads and executives.

Additional Information

Behavioural Competencies:

Analytical Thinking

Challenging Ideas

Excellent Communication Skills

Interpreting Data

Team Player

Technical Competencies:

Python (Pandas, NumPy, Scikit-learn)

Relational and No SQL/Vector Databases

ML/AI Orchestration Frameworks

Machine Learning Model Development

Research & Information Gathering

• Mining vast, unstructured financial datasets
• Build production-grade machine learning solutions
• Own the full data lifecycle—from initial mining and feature engineering to model training, validation, and deployment
• Set the standard for statistical rigor within the team
• Ensure models remain accurate, explainable, and compliant with financial regulations
• Apply advanced statistical techniques to tackle challenges in personalisation and default prediction

• Data mining
• Feature engineering
• Model training, validation, and deployment
• Statistical rigor
• Advanced statistical techniques
• Personalisation
• Default prediction
• SAS, R, SPSS
• Power BI, Tableau
• SQL, Python, Power Query, QlikView, Tableau, R
• Technical business intelligence
• Project management
• Building predictive models (credit scoring, propensity models, churn prediction, product recommendation)
• Leading data analytics teams
• Quantitative analysis
• Statistical modelling
• Stakeholder management
• Translating technical concepts
• Analytical Thinking
• Challenging Ideas
• Excellent Communication Skills
• Interpreting Data
• Team Player
• Python (Pandas, NumPy, Scikit-learn)
• Relational and No SQL/Vector Databases
• ML/AI Orchestration Frameworks
• Machine Learning Model Development
• Research & Information Gathering

• First Degree
• Degree in Information Technology, Computer Science, Actuarial Science, Statistics, Mathematics, Economics or any other related field.