Senior Quantitative Analyst

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).