Senior Data Scientist, Methodologies
- Salary
- ₹15–26 LPA
- Location
- Remote - EU
- Experience
- Senior
About company
Join the Tilt team At Tilt, we see a side of people that traditional lenders miss. Our mobile-first products and machine learning-powered credit models look beyond credit scores, using over 250 real-time financial signals to recognize real potential. With millions of customers worldwide , we're not just changing how people access financial products — we're creating a new credit system that backs the working, whatever they're working toward. The Opportunity: At Tilt, we don’t just see numbers on a spreadsheet; we see the people behind them. As The People’s Credit Company, we’re on a mission to kick down the doors of traditional finance. To do that, we need more than just technical expertise—we need a Senior Data Scientist who blends analytical grit with deep emotional intelligence. You’ll join our Methodologies group, the team responsible for building the foundational models that power our "people-first" philosophy. While others might play it safe with legacy credit scoring, you’ll be in the trenches using deep learning to find the truth in financial behavior. You’ll explore how neural networks, embeddings, and transformers can turn complex data into fair, accessible credit for everyone. As a Senior member of the team, you are a high-impact contributor who has moved beyond executing tasks to mastering the influence of the work. You are a bridge-builder—bold enough to challenge the status quo and thoughtful enough to bring your cross-functional partners along with you. How You'll Make an Impact Architecting the Future: You won't just run experiments; you’ll own the development and testing of deep learning architectures (transformers, embeddings, and NNs) that define how Tilt understands risk. Driving Strategy Across Teams: You’ll partner with peers in Engineering and Product to ensure our models aren't just "smart" in a lab, but effective and scalable in the real world. Translating Insights into Solutions: You’ll look past the symptoms of a data problem to find the ro