Staff Data Scientist, Methodologies

This is a fully remote position, open to applicants in United States.

📋 Description

• Create and evaluate modeling techniques utilizing deep learning frameworks such as transformers, embeddings, and neural networks.

• Develop and enhance frameworks that integrate seamlessly with existing ML pipelines for credit modeling and loss prediction.

• Work collaboratively with data scientists, engineers, and product partners to convert complex concepts into actionable solutions.

• Conduct experiments with pretraining and fine-tuning techniques in representation learning applied to financial datasets.

• Communicate insights and mentor colleagues to foster the growth of deep learning expertise.

• Assess model effectiveness through customer and partner outcomes, focusing beyond mere accuracy.

• Utilize advanced development tools like Claude Code and Codex for experimentation, feature engineering, and model iteration.

• Design and implement specialized agents that facilitate data science and business operations.

• Contribute to the evolution of foundational modeling techniques across Tilt’s products, enhancing access to fairer credit.

⛳️ Requirements

• Proficiency in deep learning, including neural networks, embeddings, or transformers.

• Familiarity with modern ML frameworks such as PyTorch or TensorFlow.

• Experience applying machine learning to real-world challenges, preferably in the financial services sector.

• Strong ability to link data science initiatives to measurable business results.

• Ability to communicate technical concepts clearly and thoughtfully.

• Bonus: practical experience in building with LLMs or agent frameworks, or a strong willingness to learn.

• Openness to learn, experiment, and work collaboratively.

🏝️ Benefits

• WFH office reimbursement.

• Competitive compensation packages.

• Generous equity options.

• Flexible health plans available at every premium level.

• Significant health-plan subsidies.

• Direct access to the leadership team.

• Paid global onsite meetings twice a year.

• Opportunities for growth based on contributions rather than strict promotion timelines.