Tilt seeks a Lead Data Scientist, Methodologies to build and advance foundational modeling approaches for financial data. You will explore how deep learning methods can enhance our understanding of financial data and lending decisions, working within a small, experienced team to apply these techniques to real challenges.

What You'll Do

Develop and test new modeling approaches using deep learning architectures such as transformers, embeddings, and neural networks.
Build and refine frameworks that integrate into Tilt’s existing ML pipelines to improve credit modeling and loss prediction.
Work closely with data scientists, engineers, and product partners to translate complex ideas into practical solutions.
Experiment with modern representation learning techniques — including pretraining and fine-tuning — on real financial data.
Share findings and mentor others, helping the team grow its deep learning capability.
Measure the impact of your models not just by accuracy, but by how they improve outcomes for Tilt’s customers and partners.

What We're Looking For

Experience with deep learning — neural networks, embeddings, or transformers.
Comfort working with modern ML frameworks (PyTorch, TensorFlow, or similar).
Experience applying ML to real-world problems.
Strength in connecting data science to measurable business outcomes.
Clear, thoughtful communication — you can explain technical ideas in ways others can engage with.

Nice to Have

Experience applying ML in financial services (credit, lending, payments, or banking).

Technical Stack

PyTorch, TensorFlow
Deep learning architectures: transformers, embeddings, neural networks

Team & Environment

You will join a small, experienced team of data scientists and engineers within the Methodologies group at Tilt. Our culture chases excellence and impact, stands behind our work, and fosters an environment where every voice is valued. We are working to expand access to fair credit.