Data Scientist I: Fraud & AML GenAI Solutions

Mozn; Riyadh; Vor Ort; SAR 120.000 - 180.000; Vollzeit

Benefits dieser Stelle

Competitive pay

Health insurance

Autonomy & growth

Dynamic workplace

Diversity & inclusion

Zusammenfassung

Mozn in Riyadh is seeking a Data Scientist I to support development of analytical and AI-driven solutions for fraud detection and AML risk mitigation. You will work under supervision to build and test ML and GenAI models, prep and analyze data, and stay current with industry trends in financial crime tech.

The role emphasizes learning, collaboration, and applying basic ML concepts to real-world KYC/AML challenges using SQL and Python.

Qualifikationen

• Bachelor’s degree in Data Science, AI, Machine Learning, Mathematics, Statistics, or a related field.
• 0–2 years of experience in data science or analytics.
• Basic understanding of ML concepts and exposure to Generative AI or LLMs.
• Proficiency in SQL and Python.
• English proficiency; Arabic is a plus.

Aufgaben

• Support the development of fraud detection, AML, and Generative AI use cases.
• Assist in building and testing ML and GenAI models (e.g., prompt engineering, RAG pipelines).
• Prepare, clean, and analyze structured and unstructured data.
• Support the training, evaluation, and deployment of ML models.
• Assist with experimentation using LLMs for tasks such as document analysis, alert summarization, or data enrichment.
• Execute assigned tasks within project plans under supervision.
• Stay informed about industry trends related to fraud, AML, and Generative AI.

Kenntnisse

SQL

Python

Data analysis

Problem solving

Ausbildung

Bachelor's degree in DS

Jobbeschreibung

Mozn in Riyadh is seeking a Data Scientist I to support development of analytical and AI-driven solutions for fraud detection and AML risk mitigation. You will work under supervision to build and test ML and GenAI models, prep and analyze data, and stay current with industry trends in financial crime tech.

The role emphasizes learning, collaboration, and applying basic ML concepts to real-world KYC/AML challenges using SQL and Python.