Decision Science Lead

Hire Resolve; Cape Town; On-site; ZAR 700,000 - 900,000; Full time

Job summary

A leading Commercial Bank in Stellenbosch is looking for a Decision Science Lead to oversee credit decision analysis. This role involves ensuring output alignment with credit science standards and supervising a team of experts in transforming business data into strategic insights. Candidates must have an honours degree in Mathematics or Data Science and at least 4 years of statistical analysis experience. Proficiency in programming and strong analytical skills are essential. Apply now to take the next step in your career.

Qualifications

• Minimum 4 years in statistical analysis.
• Experience with Extracting and Aggregating data within the retail Credit Risk Management Environment.
• Experience with Data Mining and Predictive Modelling.

Responsibilities

• Ensure outputs align with credit decision science standards.
• Supervise experts in analysis and strategic information.
• Conduct analysis for actionable strategic knowledge.

Skills

Communications Skills

Numerical Reasoning skills

Analytical Skills

Reporting Skills

Computer Literacy (MS Word, MS Excel, MS Outlook)

Attention to Detail

Programming skill (SAS, SQL, R, Python)

Commercial Thinking Skills

Interpersonal & Relationship management Skills

Leadership Skills

Knowledge in Analytics

Predictive Modelling and Machine Learning

Project management methodologies

General business acumen

Best practices and tools in credit risk

Retail credit environment

Confidentiality and intellectual property implications

Interpretation of user requirements

Education

Honours degree in Mathematics or Data Science

Master’s degree in Mathematics or Data Science

Job description

A leading Commercial Bank is looking for a Decision Science lead to join their team at their offices in Stellenbosch.

Key Responsibilities

• Making certain that the outputs under the purview of responsibility are in line with the goals, schemes, procedures, and standards of credit decision science (Granting Model/Collections, Recoveries, Account Management)
• To supervise a group of experts from the (Collections, Recoveries and Account Management / Granting Model) function who carry out analysis to convert live business data into actionable strategic information to inform the company on any crucial and quantifiable credit and risk indicators.
• Conduct analysis to convert current business data into actionable strategic knowledge that will advise the company about any important and quantifiable credit and risk indicators.

Minimum Requirements

• Honours degree in Mathematics or Data Science
• Master’s degree in mathematics or Data Science (Advantageous)
• Minimum 4 years in statistical analysis
• Experience with Extracting and Aggregating data within the retail Credit Risk Management Environment
• Experience with Data Mining and Predictive Modelling

Skills required

• Communications Skills
• Numerical Reasoning skills
• Analytical Skills
• Reporting Skills
• Computer Literacy (MS Word, MS Excel, MS Outlook)
• Attention to Detail
• Programming skill (SAS, SQL, R, Python)
• Commercial Thinking Skills
• Interpersonal & Relationship management Skills
• Leadership Skills
• Knowledge in Analytics
• Predictive Modelling and Machine Learning
• Project management methodologies
• General business acumen to identify the impact technical issues may have on design and delivery of solutions.
• Best practices and tools in credit risk
• Retail credit environment / industry
• Confidentiality and intellectual property implications and constraints
• Interpretation of user requirements

If you meet the above requirements and want to make a career-changing move, apply today by either filling in the online application form or by emailing your CV to [e-mail]

You are welcome to connect with Merina Naicker, Shannon Watson or Lindy-Lee du Plessis on LinkedIn or contact them on 021 551 8638 and use any of the following extensions; Merina 504, Shannon or Lindy on 514

Please note that correspondence will only be conducted with shortlisted candidates for this position. Please consider your application unsuccessful if you do not hear from us within 3 days.