Data Scientist
9 hours ago
Role: Data Scientist Marketing & Credit Risk
As a Data Scientist at Olyv, you will be part of a high-impact team driving data-led decision-making across both Marketing and Credit Risk verticals. You will design, develop, and deploy advanced machine learning models that fuel growth strategies, customer engagement, and risk management decisions.
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
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