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:

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Credit risk scoring

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Fraud detection

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Customer segmentation and Customer Lifetime Value (CLV)

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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

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📌 Data Scientist (Buenos Aires)
🏢 Accelone
📍 Buenos Aires