Data Scientist - Fraud Analytics

Capitex Open · verified Oct 24, 2026

On-site Kuwait City Mid-level Data Science

At a glance

Mid-level Data Science role at Capitex. Kuwait City.

Pay not stated Data Scientist salary

RoleData Science

SeniorityMid-level

LocationKuwait City

WorkplaceOn-site

PostedMar 3, 2026 · 7mo ago

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Build, test and launch models that detect fraud in digital banking and payment systems
Study transaction data for fraud patterns, new threats and unusual customer behaviour
Tune existing detection methods and track performance, including false positives
Work with fraud risk, rule-writing and technology teams to turn findings into controls
Document and validate models, support UAT, and monitor results after deployment

What you bring

Practical experience developing fraud models in banking, fintech or financial services
Working knowledge of machine learning methods and Python, R or a similar analytical language
Strong SQL skills and experience handling large transaction datasets
Understanding of fraud types such as account takeover, card-not-present fraud or mule accounts

Who this fits

This suits a data scientist focused on fraud analytics and model development. The role is a fixed-term contract lasting twelve months and is based in Kuwait.

From the employer

Job Title: Data Scientist – Fraud Analytics

Location: Kuwait
Contract Type: 12-Month Fixed-Term Contract

Role Overview

We are seeking a highly analytical and experienced Data Scientist – Fraud Analytics to join our team on a 12-month contract in Kuwait. The successful candidate will be responsible for developing, enhancing, and optimising fraud detection models and analytics strategies across digital and payment channels.

This role will focus on leveraging advanced analytics, machine learning, and statistical techniques to strengthen fraud detection capabilities, reduce losses, and improve customer experience by minimising false positives.

Key Responsibilities

Develop, validate, and deploy fraud detection models across digital banking and payment systems.
Perform advanced data analysis to identify fraud patterns, emerging threats, and behavioural anomalies.
Optimise existing fraud detection strategies through model tuning and performance monitoring.
Conduct feature engineering and model performance evaluation using appropriate metrics (e.g., precision, recall, AUC, false positive rate).
Collaborate with fraud risk, rule writing, and technology teams to translate analytical insights into actionable controls.
Support model governance processes including documentation, validation, and regulatory compliance requirements.
Analyse large datasets to uncover trends and recommend improvements to fraud prevention strategies.
Assist with model implementation, testing (UAT), and post-deployment performance monitoring.
Stay current with emerging fraud typologies and advancements in machine learning techniques.

Required Skills & Experience

Proven experience in fraud analytics and model development within banking, fintech, or financial services.
Strong knowledge of machine learning techniques (e.g., logistic regression, decision trees, random forests, gradient boosting, neural networks).
Hands-on experience with Python, R, or similar analytical programming languages.
Strong SQL skills and experience working with large transactional datasets.
Understanding of fraud typologies including account takeover, card-not-present fraud, mule accounts, and social engineering.
Experience with model performance monitoring and optimisation.
Strong analytical thinking and problem-solving skills.

Preferred Qualifications

Experience working within Middle Eastern financial institutions is advantageous.
Knowledge of fraud platforms such as FICO, Actimize, Feedzai, Featurespace, or similar is desirable.
Familiarity with model risk management and regulatory expectations.
Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or related discipline.