Job description

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

Requirements

Must-haves

AI & Data Center of Excellence – Abu Dhabi, UAE
Experience Bands
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
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

Nice-to-haves

Exposure to Large Language Models (LLMs) and applied AI solutions
Evaluation Criteria
Preferred Profile

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpythonsqlawsazuregcp

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