Data Scientist

Description

Summary: Design and deliver advanced analytical and machine learning solutions for financial decision-making within a regulated environment. Highlights: 1. Influence core financial decision-making with advanced analytics 2. Develop scalable ML models for measurable business impact 3. Focus on financial services use cases in a regulated environment **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