Data Scientist

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:

• 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

• Python (mandatory) • R or Scala (optional)

• Scikit-learn • TensorFlow • PyTorch • XGBoost

• Deep Learning • Natural Language Processing (NLP) • Time Series modeling • Graph Analytics

• SQL • Spark • Hive • Big Data ecosystems

• AWS • Azure • Google Cloud Platform (GCP)

• 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