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:

• → 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

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

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

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