AI & Data Center of Excellence – Abu Dhabi, UAE
Role Overview
As a Data Scientist within the AI & Data Center of Excellence, you will design and deliver advanced analytical and machine learning solutions that directly influence core financial decision-making across lending, risk, collections, and customer engagement.
This role requires a strong blend of statistical rigor, business acumen, and production-oriented thinking, with a clear focus on financial services use cases. You will work closely with cross-functional teams to build scalable models that generate measurable business impact in highly regulated financial environments.
Experience Bands
Senior Data Scientist: 8–10 years of experience
Mid-Level Data Scientist: 5–7 years of experience
Key Responsibilities
Develop and deploy machine learning models across critical financial use cases, including:
Credit risk scoring
Fraud detection
Customer segmentation and Customer Lifetime Value (CLV)
Collections optimization
Translate complex business problems into analytical frameworks and measurable outcomes
Perform exploratory data analysis on structured and unstructured datasets (e.g., transactions, call logs, financial records, documents)
Design scalable machine learning pipelines in collaboration with Data and AI Engineering teams
Lead model validation, explainability, and regulatory compliance processes (e.g., IFRS9, Basel guidelines)
Build reusable data science components, models, and accelerators
Present insights, recommendations, and model performance results to senior stakeholders
Financial Services Use Cases (Mandatory Exposure)
Candidates will be evaluated based on hands-on experience in one or more of the following areas:
Credit underwriting models (Retail, MSME, or Microfinance)
Fraud detection and Anti-Money Laundering (AML) analytics
Early Warning Systems (EWS) for credit risk monitoring
Collections prioritization and recovery optimization models
Customer 360 analytics and personalization strategies
Technical Skills
Programming Languages
Python (mandatory)
R or Scala (optional)
Machine Learning Frameworks
Scikit-learn
TensorFlow
PyTorch
XGBoost
Advanced Techniques
Deep Learning
Natural Language Processing (NLP)
Time Series modeling
Graph Analytics
Data Platforms
SQL
Spark
Hive
Big Data ecosystems
Cloud Platforms
AWS
Azure
Google Cloud Platform (GCP)
Preferred
Exposure to Large Language Models (LLMs) and applied AI solutions
Evaluation Criteria
Candidates will be evaluated based on:
Depth of real-world deployed use cases (beyond experimentation or academic projects)
Demonstrated business impact (e.g., revenue improvement, risk reduction, operational efficiency)
Experience managing the full model lifecycle (development deployment monitoring)
Understanding of financial services and risk-based decision-making environments
Key Performance Indicators (KPIs)
Model accuracy, stability, and explainability
Measurable business impact (e.g., NPL reduction, fraud detection improvement)
Speed and efficiency in delivering production-ready machine learning solutions
Reusability and scalability of developed analytical assets
Preferred Profile
Previous experience working in financial institutions such as Banks, NBFCs, or Microfinance organizations
Strong communication skills with the ability to explain complex technical concepts to business stakeholders
Ability to operate effectively in cross-country or distributed team environments
Strong ownership mindset and results-oriented approach
