AI & Data Center of Excellence – Abu Dhabi, UAE
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.
- Senior Data Scientist: 8–10 years of experience
- Mid-Level Data Scientist: 5–7 years of experience
- 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
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
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
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
- 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
- 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
