Data Scientist - Risk Analytics

Placements24; Pretoria; Hybrid; ZAR 700,000 - 1,100,000; Full time

Benefits offered by this job

Competitive salary

Health benefits

Hybrid work model

Learning opportunities

Job summary

Placements24 in Pretoria is seeking a skilled Data Scientist specializing in Risk Analytics to join our innovative team. You will apply advanced statistical modeling and machine learning to identify, measure, and mitigate financial risks, working with large datasets and delivering data-driven insights to risk management.

This hybrid role offers collaboration in the office with flexibility for remote work and opportunities to work with cutting-edge analytics infrastructure.

Qualifications

• Master's or PhD in a quantitative field such as Data Science or Statistics.
• 3–5 years of data science experience in financial risk modeling.
• Proficiency in Python or R with ML libraries.

Responsibilities

• Develop ML models for credit risk, market risk, and operational risk.
• Analyze large datasets to identify risk patterns and anomalies.
• Build predictive models to forecast losses and portfolio risk.
• Translate business needs into data science solutions.
• Design and run A/B tests and validate model performance.

Skills

Python

R

Machine Learning

Statistical Modeling

SQL

Big Data (Spark/Hadoop)

Education

Master's degree or PhD in a quantitative field

Tools

scikit-learn

TensorFlow

PyTorch

Job description

About the Role

Our client is looking for a skilled Data Scientist specializing in Risk Analytics to join their innovative team in Pretoria. This role focuses on leveraging advanced statistical modeling and machine learning techniques to identify, measure, and mitigate financial risks. You will work with large datasets, develop predictive models, and provide data-driven insights to support risk management strategies. This hybrid position allows for collaboration with colleagues in the office while providing flexibility for remote work, ideal for professionals who excel in both structured analysis and independent research.

Key Responsibilities

• Develop and implement machine learning models for credit risk, market risk, and operational risk assessment.


• Analyze large and complex datasets to identify patterns, trends, and anomalies related to financial risk.


• Build predictive models to forecast potential financial losses and assess portfolio risk exposure.


• Collaborate with risk management teams to translate business needs into data science solutions.


• Design and execute A/B tests and other experiments to validate model performance.


• Communicate findings and model insights effectively to both technical and non-technical audiences.

Requirements

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field.


• 3-5 years of experience in data science, with a focus on financial risk modeling.


• Proficiency in programming languages such as Python or R, and libraries like scikit-learn, TensorFlow, or PyTorch.


• Strong understanding of statistical modeling, machine learning algorithms, and data mining techniques.


• Experience with SQL and big data technologies (e.g., Spark, Hadoop).


• Familiarity with regulatory requirements in the financial services industry is a plus.

Benefits

• Competitive salary and performance-based bonuses.


• Comprehensive health and wellness benefits package.


• Opportunities for continued learning, conference attendance, and professional growth.


• Access to cutting-edge technology and large-scale data infrastructure.


• A supportive hybrid work model fostering both collaboration and individual focus.