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Saturday, April 19

FNB Quantitative Analyst Graduate – Data Science & Actuarial Training Program

By 4 Mins Read
FNB Banking Training Program

First National Bank (FNB) is looking for high-potential graduates with strong quantitative, actuarial, and AI skills to join its Graduate Quantitative Analyst Program.

This structured training opportunity is ideal for individuals with a passion for AI-driven decision-making, predictive modelling, and financial mathematics.

As a Graduate Quantitative Analyst, you will work on real-world financial models, employee benefits, retirement funds, and risk calculations. You will also develop AI and machine learning solutions to enhance decision-making across FNB’s financial services.

This is an excellent opportunity to gain hands-on experience in the banking and actuarial sectors while working with some of the best minds in quantitative analytics.

Location: Johannesburg, South Africa
Application Closing date: 27 March 2025
Program Type: Graduate Training | AI & Predictive Modelling

Why Choose FNB’s Quantitative Analyst Program?

  • AI & Data Science Exposure – Work with advanced predictive models, AI algorithms, and machine learning frameworks.
  • Python & SAS Development – Gain experience in Python, SAS, and financial modelling tools.
  • Actuarial & Quantitative Finance Training – Learn financial mathematics, contingencies, and actuarial risk management.
  • Real-World Financial Applications – Work on pricing, risk, and benefits modelling in a corporate environment.
  • Fast-Track Career Growth – Be part of a structured training program that prepares you for long-term roles in banking and finance.
  • Competitive Compensation & Learning Support – FNB provides industry-leading resources to ensure graduate success.

Who Should Apply?

This program is designed for graduates with a background in:

  • Data Science & AI – AI-focused thesis, machine learning, predictive analytics.
  • Actuarial Science – Actuarial exams in Financial Mathematics (A211), Contingencies (A213), Actuarial Risk Management (A311).
  • Statistics & Mathematics – Strong quantitative modelling and analytical skills.
  • Finance & Economics – Understanding of pricing models, risk assessment, and financial forecasting.
  • Computer Science & Engineering – Programming skills in Python and SAS for data-driven decision-making.

This role is ideal for high-achieving graduates who want to apply their quantitative skills in banking, AI, and actuarial modelling.

Key Responsibilities – What You’ll Do

As a Graduate Quantitative Analyst, your work will focus on:

  • AI & Predictive Modelling: Apply machine learning techniques to credit risk, financial forecasting, and pricing models.
  • Data Sourcing & Management: Work with structured and unstructured data sets to enhance analytics.
  • Actuarial & Financial Modelling: Use quantitative models for risk management, employee benefits, and investment planning.
  • Banking & Insurance Analytics: Develop solutions for pension funds, disability benefits, and actuarial product pricing.
  • Data Programming & Automation: Utilize Python and SAS for automated data processing and financial model execution.
  • Strategic Business Insights: Deliver data-driven insights to improve decision-making across financial services.

This challenging and rewarding opportunity offers hands-on experience in banking, insurance, and financial technology (FinTech).

Not Qualified Yet? Here’s How to Qualify

If you do not yet meet the full eligibility criteria, consider enrolling in specialized courses and certifications that can strengthen your qualifications for this role.

University of Cape Town (UCT) – Data Science & Predictive Analytics
Wits Business School – Financial Modelling & AI in Banking
Institute of Actuaries (UK) – Actuarial Certification (CT Exams)
Harvard Online – Machine Learning & AI for Finance
MIT OpenCourseWare – Quantitative Finance & Risk Management
Coursera – Python for Data Science & Financial Modelling

Gaining certifications in data science, actuarial risk management, and AI modelling will significantly boost your application chances.

How to Apply (Step-by-Step Process)

Step 1: Prepare Required Documents

  • Updated CV – Highlight data science, AI, actuarial, or finance experience.
  • Academic Transcripts – Show actuarial exams, financial mathematics coursework, or AI-related studies.
  • Reference Letters – From lecturers, professors, or previous employers.

Step 2: Submit Your Application

  • Ensure all documents are uploaded before the deadline.
  • Apply online via the FNB Careers portal below:
Apply online

Step 3: Selection & Interview Process

  • Stage 1: Application screening by FNB Talent Team.
  • Stage 2: Psychometric assessments (quantitative reasoning & problem-solving).
  • Stage 3: Final interview with FNB executives and data science team.