lead-decision-science-at-weplace

Job Description:

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wePlace is a specialist recruiter. We find and place the best talent available in the market by headhunting individuals with specific skill sets, backgrounds and qualifications. We apply our in-depth knowledge and experience in the areas that we operate in, specifically, Information Technology, Banking, Finance, Sales and Marketing, to cater for our clients ...

Lead: Decision Science

Purpose Statement

To ensure the deliverables within the area of responsibility aligns with the objectives, plans, processes and standards of Credit Decision Science (Collections, Recoveries, Account Management)
To lead a team of specialists within the (Collections, Recoveries and Account Management) function to perform analysis to translate active business data into usable strategic information to inform the business on any critical and measurable credit and risk indicators.
To perform analysis to translate active business data into usable strategic information to inform the business on any critical and measurable credit and risk indicators.

Experience

4 years’ relevant experience in data science
Experience must include the following (within the Retail Credit Risk Management Environment):
- Extracting and Aggregating Data from Large Relational Databases
- Data Mining and Predictive Modelling
Preferred:
- Leadership demonstrates in project/ leading of analyst/ data scientist

Qualifications (Minimum)

Honours Degree in Mathematics or Data Science

Qualifications (Ideal or Preferred)

Master’s Degree in Mathematics or Data Science

Knowledge

Analytics
Predictive Modelling and Machine Learning
Project management methodologies
IT implementation cycle
Credit cycle
Technical understanding and knowledge (different operating systems / databases / programming languages)
General business acumen to identify the impact technical issues may have on design and delivery of solutions.
Best practices and tools in credit risk
Interpretation of user requirements and translation into business requirements specifications
Retail credit environment / industry
Confidentiality and intellectual property implications and constraints
Interpretation of user requirements
Translation of business requirements into business requirement specifications