[Hiring] Senior Data Scientist @Tilt Finance

Role Description

At Tilt, we're looking for a Senior Data Scientist to join our Methodologies group. This role focuses on building and advancing foundational modeling approaches.

• Build and test deep learning methods such as transformers to improve understanding of financial data and lending decisions.
• Work with a small, experienced team of data scientists and engineers, taking projects from first idea to working model on financial data challenges.
• Be at the forefront of how data science is practiced in the age of AI agents.
• Use agentic tools in your work and help build specialized agents that speed up both data science and business workflows.
• Connect technical exploration to meaningful business impact.

Qualifications

• Hands-on experience building and training deep learning models.
• 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).
• Experience taking a project from an open question to a measurable business outcome.
• Clear, thoughtful communication — ability to explain technical ideas in engaging ways.
• Bonus: hands-on experience building with LLMs or agent frameworks, or excitement to learn.

Requirements

• Build, train and evaluate deep learning models on Tilt's financial data.
• Ship models and model components that plug into Tilt's existing ML pipelines to improve credit modeling and loss prediction.
• Own projects end to end, from hypothesis to a model in use, with general direction from the team's leads.
• Work closely with data scientists, engineers, and product partners to turn results into practical solutions.
• Run representation learning experiments — including pretraining and fine-tuning — on real financial data.
• Share findings and help peers pick up new deep learning methods.
• Measure the impact of models not just by accuracy, but by how they improve outcomes for Tilt's customers and partners.
• Use agentic development tools (e.g., Claude Code, Codex) to speed up experimentation, feature engineering and model iteration.
• Build and improve specialized agents that support data science workflows.

Benefits

• Virtual-first teamwork with collaboration across 14 countries and 12 time zones.
• Competitive compensation packages and generous equity.
• Flexible health plans at every premium level, with substantial subsidies.
• Direct exposure to leadership team — good ideas travel quickly.
• Paid global onsites for team reconnections over shared meals or adventures.
• Growth opportunities follow contributions, not rigid promotion timelines.