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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

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Role Overview

Experience Bands

Key Responsibilities

Financial Services Use Cases (Mandatory Exposure)

Technical Skills

Evaluation Criteria

Key Performance Indicators (KPIs)

Preferred Profile

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