Lead Data Scientist, Methodologies
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Job Description
Lead Data Scientist at Tilt focused on building foundational modeling approaches using deep learning. Collaborating with teams to enhance understanding of financial data and lending decisions.
Responsibilities:
- 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 — including pretraining and fine-tuning — on real financial data.
- Share findings and mentor others, helping the team grow its deep learning capability.
- Measure the impact of your models not just by accuracy, but by how they improve outcomes for Tilt’s customers and partners.
Requirements:
- 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 — you can explain technical ideas in ways others can engage with.
Benefits:
- Virtual-first teamwork: The Tilt team is collaborating across 14 countries, 12 time zones, and counting. You’ll get started with a WFH office reimbursement.
- Competitive pay: We're big on potential, and it's reflected in our competitive compensation packages and generous equity.
- Complete support: Find flexible health plans at every premium level, and substantial subsidies that stand up to global standards.
- Visibility is yours: You can count on direct exposure to our leadership team — we’re a team where good ideas travel quickly.
- Paid global onsites: Magic happens IRL: we gather twice yearly to reconnect over shared meals or kayaking adventures. (We’ve visited Vail, San Diego, and Mexico City, to name a few.)
- Impact is recognized: Growth opportunities follow your contributions, not rigid promotion timelines.
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