Data Scientist, Risk

Qualifications

• First Degree Information Studies, Information Technology or related from a recognised Insitution
  
• 3-4 years experience 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 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.)
  
• 1-3 years proven experience in risk advisory based on data analysed. Understanding of risk data and how to implement risk monitoring tools. Experience in working with risk teams and providing risk mitigation intelligence. Knowledge of risk registers and how to automate insight extraction from them.
  

• 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.
  
• Performs data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation and derivation of new variables/features.
  
• Utilises advanced data analytics and mining techniques to analyse data, assessing data validity and usability; reviews data results to ensure accuracy; and communicates results and insights to stakeholders. Collaborate with subject matter experts to select the relevant sources of information and translates the business requirements into data mining/science outcomes.
  
• Supports and implements operational IA plan, rules, methodologies and coding initiatives in order to ensure IA for remediation efforts. Support and implements the strategy for productionalising automation software so that it is accurate and well maintained.
  
• Supports business integration through integrating model outputs into end-point production systems, incorporating business requirements and knowledge of best practices.