Staff Data Scientist, Methodologies

Posted 4hrs ago

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Job Description

Staff Data Scientist developing foundational deep-learning methods for Tilt’s mobile-first credit products. Building models and specialized agents to improve lending decisions and financial outcomes.

Responsibilities:

  • Develop and test modeling approaches using deep learning architectures such as transformers, embeddings, and neural networks
  • Build and refine frameworks integrating with existing ML pipelines for credit modeling and loss prediction
  • Collaborate with data scientists, engineers, and product partners to translate complex ideas into practical solutions
  • Experiment with pretraining and fine-tuning representation learning techniques on financial data
  • Share findings and mentor others to grow deep learning capability
  • Measure model impact through customer and partner outcomes, not only accuracy
  • Use agentic development tools such as Claude Code and Codex for experimentation, feature engineering, and model iteration
  • Design and ship specialized agents supporting data science and business workflows
  • Help advance foundational modeling approaches across Tilt’s products and improve access to fairer credit

Requirements:

  • Experience with deep learning, including neural networks, embeddings, or transformers
  • Comfort working with modern ML frameworks such as PyTorch or TensorFlow
  • Experience applying machine learning to real-world problems, ideally in financial services
  • Strength connecting data science to measurable business outcomes
  • Clear, thoughtful communication of technical ideas
  • Bonus: hands-on experience building with LLMs or agent frameworks, or excitement to learn
  • Willingness to learn, experiment, and collaborate

Benefits:

  • WFH office reimbursement
  • Competitive compensation packages
  • Generous equity
  • Flexible health plans at every premium level
  • Substantial health-plan subsidies
  • Direct exposure to the leadership team
  • Paid global onsites twice yearly
  • Growth opportunities based on contributions rather than rigid promotion timelines