Job Description

Assist in applying data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. Model 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.

Qualifications

Experience Required

• 3–4 years’ experience in Data & Analytics / Data Monetisation
• Experience working with structured and unstructured data
• Strong knowledge of data architecture, ETL and data flows
• Experience in data mining and statistical analysis
• Proficiency in SAS, R or Python and visualisation tools (Power BI, Tableau)
• Experience in machine learning modelling and predictive analytics
• Understanding of banking data, systems and products
• Exposure to software engineering, BI and project delivery

Essential Functions

• Develop and deploy machine learning and statistical models to solve business problems
• Perform data preprocessing, cleansing and feature engineering to improve model performance.
• Analyse large datasets to identify patterns, trends and insights for decision-making.
• Design and maintain data pipelines and ETL processes to support analytics.
• Translate business needs into data science solutions in collaboration with stakeholders.
• Build and maintain predictive models (credit risk, churn, propensity).
• Present insights through visualisations and reports to business stakeholders.
• Support model deployment and integration into production systems and ensure performance.

Additional Information

Behavioural Competencies:

• Articulating Information
• Challenging Ideas
• Examining Information
• Interpreting Data
• Meeting Timescales
• Producing Output
• Providing Insights

Technical Competencies:

• Data Analysis
• Database Administration
• Data Integrity Management
• Machine Learning & Modeling
• Knowledge Classification
• Research & Information Gathering

• Develop and deploy machine learning and statistical models to solve business problems.
• Perform data preprocessing, cleansing and feature engineering to improve model performance.
• Analyse large datasets to identify patterns, trends and insights for decision-making.
• Design and maintain data pipelines and ETL processes to support analytics.
• Translate business needs into data science solutions in collaboration with stakeholders.
• Build and maintain predictive models (credit risk, churn, propensity).
• Present insights through visualisations and reports to business stakeholders.
• Support model deployment and integration into production systems and ensure performance.

• Data mining
• Statistical analysis
• SAS, R or Python
• Power BI, Tableau
• Machine learning modelling
• Predictive analytics
• Data architecture
• ETL
• Data flows
• Data preprocessing
• Data cleansing
• Feature engineering
• Data pipelines
• Visualisation
• Software engineering
• BI
• Project delivery
• Articulating Information
• Challenging Ideas
• Examining Information
• Interpreting Data
• Meeting Timescales
• Producing Output
• Providing Insights
• Data Analysis
• Database Administration
• Data Integrity Management
• Machine Learning & Modeling
• Knowledge Classification
• Research & Information Gathering

• 3–4 years’ experience in Data & Analytics / Data Monetisation
• Experience working with structured and unstructured data
• Strong knowledge of data architecture, ETL and data flows
• Experience in data mining and statistical analysis
• Proficiency in SAS, R or Python and visualisation tools (Power BI, Tableau)
• Experience in machine learning modelling and predictive analytics
• Understanding of banking data, systems and products
• Exposure to software engineering, BI and project delivery