About the job
Job Title: Data Scientist – Fraud Analytics
Location: Kuwait
Contract Type: 12-Month Fixed-Term Contract
Role Overview
Join our dynamic team at Capitex as a Data Scientist – Fraud Analytics in a pivotal role where your analytical expertise will help us combat fraud effectively. This 12-month contract position in Kuwait involves the development and optimization of sophisticated fraud detection models and analytics strategies tailored for digital and payment channels.
Your contributions will leverage advanced analytics, machine learning, and statistical methodologies to enhance our fraud detection capabilities, minimize losses, and enrich the customer experience by decreasing false positives.
Key Responsibilities
- Design, validate, and implement fraud detection models across digital banking and payment frameworks.
- Conduct detailed data analysis to uncover fraud patterns, potential threats, and behavioral irregularities.
- Refine existing fraud detection strategies through model tuning and performance assessment.
- Engage in feature engineering and evaluate model performance using relevant metrics (e.g., precision, recall, AUC, false positive rates).
- Collaborate with cross-functional teams, including fraud risk specialists, rule authors, and technology experts to convert analytical insights into effective controls.
- Facilitate model governance processes involving documentation, validation, and adherence to regulatory standards.
- Analyze extensive datasets to identify trends and propose enhancements to our fraud prevention strategies.
- Support model deployment, testing (UAT), and monitor post-implementation performance.
- Stay updated on emerging fraud schemes and advancements in machine learning technologies.
Required Skills & Experience
- Demonstrated experience in fraud analytics and model development within the banking, fintech, or financial services sectors.
- In-depth knowledge of machine learning algorithms (e.g., logistic regression, decision trees, random forests, gradient boosting, neural networks).
- Proficiency in analytical programming languages such as Python or R.
- Strong SQL capabilities with experience handling large transactional datasets.
- Familiarity with various fraud typologies, including account takeovers, card-not-present fraud, mule accounts, and social engineering tactics.
- Background in model performance monitoring and optimization.
- Exceptional analytical reasoning and problem-solving skills.
Preferred Qualifications
- Experience with Middle Eastern financial institutions is a plus.
- Knowledge of fraud detection platforms like FICO, Actimize, Feedzai, or Featurespace is advantageous.
- Understanding of model risk management and compliance expectations.