Data Scientist ( Finance and Value Management )

To apply advanced data mining, statistical analysis, machine learning, and visualisation techniques to large structured and unstructured datasets in order to identify trends, generate insights, and solve complex business problems, while working closely with business, data, and technology teams to transform data into actionable information that supports sound decision-making. The role also drives revenue assurance strategies, governance frameworks, and risk mitigation initiatives to minimise revenue leakage, overseeing systems, data analytics, reporting, and continuous process improvements to strengthen revenue leakage management, prevention, and operational effectiveness across the Bank.

Qualifications

Type of Qualification: First Degree
Field of Study: Actuarial Science
Type of Qualification: First Degree
Field of Study: Financial Mathematics or Information Technology

Experience Required
Data Monetisation
Data & Analytics
3-4 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.

3-4 years
Proven development experience in software and software engineering. 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 models (credit scoring, propensity models, churn, etc.)

Additional Information

Behavioural Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Checking Things
• Examining Information
• Exploring Possibilities
• Interacting with People
• Interpreting Data
• Meeting Timescales
• Producing Output
• Providing Insights
• Team Working

Technical Competencies:

• Data Analysis
• Database Administration
• Data Integrity
• Knowledge Classification
• Research & Information Gathering