Data Scientist

About the role

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