Companies in this catalog do not charge applicants. Never send money to apply.
Minimum requirements:
3-5 years relevant of professional experience in an analytical and technical environment, with a focus on innovation and applying data-driven approaches to solve complex problems.
Advanced Diplomas/National 1st Degrees
Professional Qualifications/Honours Degree
Post graduate degree in mathematics/statistics/actuarial science/engineering/data science or a related quantitative discipline.
Master's degree is preferred
Technical / professional knowledge:
Business Acumen
Microsoft Office
Risk management process and frameworks
Strong analytical skills: Demonstrated ability to analyse large datasets, identify patterns, and draw meaningful conclusions. Experience with statistical analysis, data mining, and machine learning techniques is essential.
Problem-solving mindset: Proven track record of identifying innovative solutions to business challenges using data-driven approaches. Ability to think critically, creatively, and outside the box.
Relevant software and systems knowledge:
Programming languages: Proficiency in one or more programming languages commonly used in data science and quantitative analytics.
Data analysis and visualisation: Experience with data analysis libraries and frameworks. Familiarity with data visualisation tools like Power BI is a plus.
Machine learning: Strong understanding of machine learning algorithms and their applications.
Extensive understanding in data pipelines with adequate knowledge of data and development/deployment systems and architecture?
Automation techniques: Experience in using advanced techniques and tools to automate processes.
Model Governance: Knowledge of model governance principles, the regulatory landscape and modelling best practices, ensuring quality, integrity, and compliance throughout the model life-cycle.
(Bonus) Credit process knowledge: Familiarity with credit assessment and evaluation processes in a business context.
Understanding of credit risk modelling, credit scoring, and credit underwriting practices.
Communication skills: Excellent verbal and written communication skills to effectively present findings and insights to both technical and non-technical stakeholders.
Ability to explain complex concepts in a clear and concise manner (business writing skills).
Collaboration skills: Ability to work collaboratively with cross-functional teams, such as credit analysts, business stakeholders, and IT professionals, to gather requirements and implement automated solutions effectively.
Responsibilities:
Design and develop superior innovative quantitative solutions to service stakeholder and business requirements across the group encompassing AI/ML initiatives across clusters, Credit Risk, Financial Crime, People Risk (HR analytics), Compliance and Conduct Risk analytics, supporting Group Internal Audit (GIA) through analysis
Drive multiple group wide strategic initiatives relating to AI and ML.
Model and methodology advisory and support for all clusters (solution generator, unlock business and client value).
Challenge model builds through expert group and model technical forum participation, contribute to the development of differentiated, superior solutions and ensuring best practice.
Seek opportunities to improve business processes, models and systems by identifying and recommending effective ways to operate and adding value to the company.
Build relationships with stakeholders by networking through targeted and informal interactions and consistent delivery of quality output to build trust.
Enhance applicable group frameworks and policies and participated in annual review processes.
Increase efficiencies through programming and automating processes.
Build and formally present reports by monitoring business performance within the set risk appetite and through analysis.
Report to, monitor and advise operational areas to manage trends through analysis for ad-hoc requirements.
Ensure personal growth and enable effectiveness in performance of roles and responsibilities through formal and informal learning activities and practical experience.
Enable upskilling and required corrective action to take place by sharing knowledge and industry trends with team and stakeholders during formal and informal interactions.
Improve personal capability and stay abreast of developments in field of expertise by identifying training courses and career progression for self through input and feedback from managers.
Contribute to a culture conducive to the achievement of transformational goals by participating in Nedbank Culture building initiatives (e.g. staff surveys etc).