Quantitative Analyst

Weplace; Cape Town; On-site; ZAR 900,000 - 1,500,000; Full time

Benefits offered by this job

Competitive rewards/benefits

Learning opportunities

Career growth

Job summary

Weplace is seeking an independent validator to assess high-impact ML models across credit risk, propensity, and financial crime areas in a hands-on technical role.

You will validate models end-to-end, apply advanced ML techniques, and work closely with Risk, Technology and Business teams to ensure governance, performance and scalability.

Qualifications

• Strong hands-on experience building machine learning and data science models end-to-end
• Advanced proficiency in Python
• Experience with independent validation of models and/or peer review

Responsibilities

• Independent validation of machine learning models across credit risk, propensity and behavioural models, and financial crime models
• Apply advanced ML techniques and ensure governance, performance and scalability
• Partner with Risk, Technology, and Business stakeholders to validate models
• Manage model risk across the end-to-end lifecycle including feature engineering, training, evaluation, deployment, and monitoring

Skills

Python

Machine Learning

SQL

Model Validation

Credit Risk

Financial Crime

XGBoost

CatBoost

Neural Networks

Anomaly Detection

Clustering

Education

Honours or Masters in Mathematics, Statistics, Computer Science, Actuarial Science, or related quantitative field

Job description

You will be independently validate highâimpact models across credit risk, financial crime, and advanced analytics use cases. This is a handsâon technical role.

What You'll Be Doing

Independent validation of machine learning models across:

• Credit risk models
• Propensity and behavioural models
• Financial crime models (fraud and AML)

Applying advanced ML techniques, including:

• Supervised learning (Random Forest, XGBoost, CatBoost, Neural Networks)
• Unsupervised learning (clustering, isolation forests)

Managing model risk across the endâtoâend model lifecycle, including:

• Feature engineering and data preparation
• Model training, evaluation, and selection
• Production deployment and monitoring
• Building and reviewing models in Pythonâbased environments

Partnering closely with Risk, Technology, and Business stakeholders. Ensuring models meet governance, performance, and scalability standard.

What We Are Looking For

58 years relevant experience Strong handsâon experience building machine learning and data science models endâtoâend Proven use of techniques such as:

• Boosting algorithms (XGBoost, CatBoost)
• Neural networks
• Clustering and anomaly detection

Advanced proficiency in Python Solid experience with SQL and working with large, complex datasets Experience within credit risk, propensity modelling, or financial crime analytics Experience with independent validation of models and/or detailed peer review Proven experience researching machine learning model

Qualifications

Honours or Masters degree in Mathematics, Statistics, Computer Science, Actuarial Science, or a related quantitative fiel

Preferred/ Ideal

Experience deploying or supporting models in cloud environments Exposure to credit risk modelling, scorecards, or IFRSârelated analytics Financial crime (fraud or AML) modelling experience Experience designing models with scalability and deployment in mind Familiarity with model risk, governance, or validation standard

What's in it for you?

• Work on highâimpact, real-world models used across the industry.
• Exposure to a wide variety of models, not just one product or portfolio.
• Learn from experienced quantitative leaders in a collaborative environment.
• Be part of a fast-growing organisation that values simplicity, transparency, and ownership.
• Competitive rewards, learning opportunities, and long-term career growth

Conditions of Employment

Clear criminal and credit record. Should you not receive a response from us within one week of your application, your application has unfortunately not been successful.