Staff Data Scientist, Methodologies

Tilt Finance

8d • $192,000 - $240,000 • Remote

About The Position

Tilt is seeking a Staff Data Scientist to join its Methodologies group and build foundational modeling approaches for financial data and lending decisions. The role explores deep learning methods—including neural networks, embeddings, and transformers—and applies them to challenges such as credit decisioning, transaction modeling, and loss prediction. The position works with a small, experienced team of data scientists and engineers to connect technical exploration with practical business impact and customer outcomes.

The role also contributes to how Tilt practices data science using AI agents. It uses agentic development tools to accelerate experimentation, feature engineering, and model iteration, and designs specialized agents that support data science and business workflows. The position includes sharing findings and mentoring others to expand the team's deep learning capabilities.

Requirements

• Experience with deep learning, including neural networks, embeddings, or transformers.
• Comfort working with modern machine learning frameworks such as PyTorch or TensorFlow.
• Experience applying machine learning to real-world problems.
• Strength in connecting data science to measurable business outcomes.
• Clear, thoughtful communication skills and the ability to explain technical ideas to others.

Nice To Haves

• Experience applying machine learning in financial services, such as credit, lending, payments, or banking.
• Hands-on experience building with large language models or agent frameworks, or enthusiasm to learn.

Responsibilities

• Develop and test modeling approaches using deep learning architectures such as transformers, embeddings, and neural networks.
• Build and refine frameworks that integrate with Tilt’s existing machine learning pipelines to improve credit modeling and loss prediction.
• Collaborate with data scientists, engineers, and product partners to turn complex ideas into practical solutions.
• Experiment with representation learning techniques, including pretraining and fine-tuning, on financial data.
• Share findings and mentor others to grow the team’s deep learning capability.
• Measure model impact by customer and partner outcomes as well as accuracy.
• Use agentic development tools, such as Claude Code and Codex, to accelerate experimentation, feature engineering, and model iteration.
• Design and ship specialized agents that support data science and business workflows.

Benefits

• Work-from-home office reimbursement.
• Competitive compensation and generous equity.
• Flexible health plans at multiple premium levels and substantial subsidies.
• Direct exposure to the leadership team.
• Paid global team gatherings twice yearly.
• Growth opportunities tied to contributions rather than rigid promotion timelines.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed