Assistant Manager / Manager

KPMG-SouthAfrica; Johannesburg; On-site; ZAR 800,000 - 1,200,000; Full time

Job summary

A leading financial services firm in Johannesburg is seeking a Manager / Assistant Manager in Credit Risk & Capital Management. You will develop and review models while managing projects and mentoring junior staff. Candidates should have over five years of experience in credit risk and possess a relevant Honours or Master’s degree in a quantitative discipline. Skills in Python, R, and SAS are essential for this role.

Qualifications

• 5+ years of experience in credit risk.
• Experience in a quantitative credit risk-based role.
• Able to read, interpret and create software code.

Responsibilities

• Development and review of credit risk models.
• Support managing projects in their various phases.
• Assist in coding and automation of financial risk models.

Skills

Quantitative analysis

Statistical techniques

Python

R

SAS

Time management

Team management

Communication skills

Education

Hons or Masters in Quantitative Finance, Mathematics, or Statistics

Tools

SAS

Python

R

Job description

Overview

Description of the role and purpose of the job :

We have an exciting opportunity to join our Financial Risk Management business unit, and we are looking to fill a Manager / Assistant Manager position in our Credit Risk & Capital Management team. We are a specialist function within KPMG's Advisory Practice which has as its main purpose to assist clients with the development and review of often complex statistical models used to quantify financial risks. The Credit Risk & Capital management team is made up of credit risk modellers and analysts from a broad and diverse range of quantitative backgrounds, including mathematics, statistics, engineering and actuarial.

As an Manager / Assistant Manager, you will not only have a technical specialist role in the auditing and the development of credit risk models (IFRS9, scorecards, etc.), but also assist the Senior Manager with responsibilities with regard to the planning, organization and quality control of projects. The role offers exposure to a wide range of modelling techniques used by banks ranging from smaller local credit providers to globally systemically important banks. It also provides access to the latest technologies and developments, and you will be able to hone your coding skills in packages like Python, R and SAS.

Key responsibilities

• Development and review of credit risk model both for provisioning and regulatory capital requirement purposes
• Support with managing projects in the planning / budgeting, execution, and close-out phases.
• Assisting with the coding and automation of financial risk management models
• Participating in building a coaching culture aimed at getting the best out of others in an environment where everyone in the team feels empowered to speak up or challenge where appropriate.

Skills and attributes required for the role

• Relevant experience within a quantitative credit risk-based role
• Well versed in contemporary statistical techniques and practices in credit risk modelling.
• Able to read, interpret and create software code, and relevant experience with modern computing languages related to credit risk modelling (SAS, Python, or R)
• Strong organisational and time management skills
• Proven track record of managing and delivering small workstreams
• Experience of managing teams, coaching and mentoring junior staff
• Able to work effectively in a fast-paced environment with conflicting priorities and deadlines.
• Good presentation and communication skills with ability to articulate quantitative concepts to both technical and non-technical individuals
• Participating in building a coaching culture aimed at getting the best out of others in an environment where everyone in the team feels empowered to speak up or challenge where appropriate.

Minimum requirements to apply for the role

• A Hons or Masters degree in a quantitative discipline such as Quantitative Finance, Mathematics, Statistics or equivalent, FRM advantageous
• At least five years of experience in credit risk