Job Description

Oversee data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. Model complex business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualisation techniques, working closely with clients, data and technology teams to turn data into critical information used to make sound business decisions. Oversee predictive modelling.

Qualifications

• Type of Qualification: First 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 models (credit scoring, propensity models, churn, 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 modeling.

Additional Information

Behavioural Competencies:

• Analytical Thinking
• Challenging Ideas
• Excellent Communication Skills
• Interacting with People
• 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