Data Scientist

Role: Data Scientist Marketing & Credit Risk

Marketing

Requirement 1: Data Scientist – Marketing

Preferred Qualifications

• 2 or more years of work experience in creating and deploying ML models
• Bachelor’s degree in Engineering or a related field
• Advanced proficiency in

  SQL and Python

  is a must
• Hands-on experience in deploying ML models into production (batch or streaming)
• Experience in building ML models for marketing campaign channels such as

  Propensity Models, Hyper-Personalization, and Next Best Action (NBA)

• Strong analytical and problem-solving skills with attention to detail and the ability to manage multiple projects with evolving priorities
• Proven experience in end-to-end ownership of projects and collaboration with multiple stakeholders
• Excellent communication and presentation skills to simplify complex findings
• Experience with

  LLMs

  and

  Deep Learning models

  will be an added advantage

Roles and Responsibilities

• End-to-end ownership of development and implementation of ML models for marketing, including design, validation, deployment, and monitoring
• Work with

  Non-Bureau and Alternate Data

  to build marketing analytics models
• Collaborate with teams such as

  Growth, Tech, Marketing, and Sales

  to enhance model usage in business decision-making
• Design and implement

  statistical and ML-based solutions

  for various marketing initiatives
• Build

  automated data pipelines

  for scalable and efficient data processing
• Mentor junior data analysts and data scientists

Requirement 2: Data Scientist – Credit RiskPreferred Qualifications

• 2 or more years of work experience in creating ML models for

  Retail Credit Risk

• Bachelor’s degree in Engineering or a related field
• Advanced proficiency in

  SQL and Python

  is a must
• Strong understanding of

  Credit Risk concepts

  – Credit Bureau data, default rates, roll rates, vintage analysis, etc.
• Experience in

  Model Validation and Monitoring

  using metrics such as

  AUC, GINI, KS, PSI, and Drift

• Must have deployed ML models into production (batch or streaming)
• Strong analytical and problem-solving skills with proven ability to manage multiple priorities
• Excellent communication and presentation skills
• Experience in

  LLMs

  and

  Deep Learning models

  will be an added advantage

Roles and Responsibilities

• End-to-end ownership of ML model development and implementation for credit risk assessment, including design, validation, deployment, and post-deployment monitoring
• Work with

  Non-Bureau and Alternate Data

  to build robust credit risk models
• Collaborate with

  Risk, Tech, and Collections

  teams to embed model insights in decision-making
• Design

  statistical and ML-based solutions

  to optimize credit strategies and risk evaluation
• Build

  automated data pipelines

  and tools for model deployment and monitoring
• Mentor junior analysts in data science practices

What You Can Expect

• A culture that encourages openness, innovation, and continuous learning
• Opportunity to make a

  meaningful impact

  by enabling data-driven decision-making across business functions
• The chance to

  uplift millions of people

  by contributing to financial inclusion
• Exposure to advanced ML, LLM, and data infrastructure frameworks in a fast-scaling fintech environment