Staff Data Scientist, Methodologies

The role

This person will build and advance foundational modeling approaches using deep learning methods. They will work with a small team to design and apply techniques like neural networks, embeddings, and transformers to financial data.

What you will 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 on real financial data.
• Share findings and mentor others to help the team grow its deep learning capability.
• Measure the impact of models by how they improve outcomes for customers and partners.
• Use agentic development tools to speed up experimentation, feature engineering and model iteration.
• Design and ship specialized agents that support DS and business workflows.

Required qualifications

• 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, ideally in financial services (credit, lending, payments, or banking).
• Strength in connecting data science to measurable business outcomes.
• Clear, thoughtful communication.

Nice to have

• hands-on experience building with LLMs or agent frameworks
• excitement to learn

Benefits

• WFH office reimbursement
• competitive compensation packages
• generous equity
• flexible health plans at every premium level
• substantial subsidies that stand up to global standards
• direct exposure to our leadership team
• Paid global onsites

About the company

Tilt uses mobile-first products and machine learning-powered credit models to recognize potential using real-time financial signals. They are creating a new credit system to help people access financial products.