Data Scientist

About Olyv

Role: Data Scientist – Marketing & Credit Risk

Marketing

Credit Risk

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 Risk

Preferred 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