Job Overview
Lead Data Scientist – Fraud Detection, AI Risk Intelligence & Behavioral Analytics (Qatar Fintech Sphere)
Location: Remote (Preference for applicants with regional knowledge of Qatar’s digital banking space)
Position Type: Full-Time – Remote
Specialization: Artificial Intelligence, Payment Fraud Prevention, Graph Analytics, Biometric Security
Job Summary:
A prestigious opportunity is now available for an advanced-level Data Scientist with deep command of machine learning applications in real-time financial fraud detection, high-volume transaction analysis, and behavioral biometrics.
This role directly contributes to building the machine intelligence backbone of a new generation of Qatar-based fintech platforms—particularly those at the intersection of AI-based risk scoring, micro-transaction monitoring, and explainable AI for compliance.
You will lead initiatives to analyze complex financial interactions, uncover fraud networks using graph-based learning, and deploy cloud-native, real-time ML risk engines integrated into digital wallets, payment processors, and neobank APIs.
Scope of Work:
📌 Advanced Model Development & Evaluation
-
Build hybrid models for fraud scoring using boosted trees, graph neural networks, and deep learning autoencoders.
-
Train and evaluate fraud detection models using millions of historical transactions enriched with geospatial, device, and behavioral metadata.
-
Develop anomaly detection systems capable of flagging synthetic identities, coordinated fraud rings, and merchant collusion.
📌 Real-Time ML Pipelines & Cloud Integration
-
Partner with DevOps to deploy containerized ML inference systems using Kubernetes, Flink, Kafka, and AWS Sagemaker.
-
Optimize fraud detection latencies under 200ms, enabling real-time decisioning without false positives that compromise user experience.
📌 Ethical AI, Regulatory Interpretability & XAI
-
Build interpretability dashboards (SHAP, LIME) to enable compliance officers and regulators to understand AI decisions at transaction and aggregate levels.
-
Ensure AI models conform to emerging Qatar Central Bank (QCB) standards for automated decisioning, fairness, and auditability.
📌 Behavioral Analytics, Biometrics & Risk Intelligence
-
Utilize behavioral signals (e.g., typing cadence, mobile accelerometer data, swipe patterns) to build behavioral fingerprints for fraud prevention.
-
Construct fraud graph topologies using Neo4j or TigerGraph to analyze abnormal fund flows, layered accounts, and coordinated SIM/device usage.
Candidate Attributes:
✅ Technical Mastery
-
6+ years in applied data science roles with direct experience in fraud analytics, preferably in MENA fintech environments.
-
Expert-level proficiency in Python (PyTorch, scikit-learn, XGBoost), SQL, Spark, and streaming ML architectures.
✅ Domain Expertise
-
Proven success working on fraud analytics for neobanks, cross-border payments, or digital wallet systems.
-
Familiarity with Qatar’s cybersecurity mandates, financial fraud typologies, and biometric data privacy regulations.
✅ Bonus Capabilities
-
Experience building federated learning pipelines or synthetic data training tools.
-
Arabic fluency or prior work in GCC tech hubs (Qatar, UAE, Bahrain).
Are you excited about this opportunity?
Don’t miss the chance to make a difference in the fintech and FX industry!
👉 Apply now by clicking on the “Apply Now” button below.
Let’s shape the future of finance together!
#EmploySolutionJobs #FXCareers
#MiddleEastJobs #UAEFinance
#NowHiring #FinancialServices #FXIndustry.
