Staff Data Scientist, Methodologies
Tilt Finance · Full-time · Posted 6d ago
At Tilt, we see a side of people that traditional lenders miss. Our mobile-first products and machine learning-powered credit models look beyond credit scores, using over 250 real-time financial signals to recognize real potential. With millions of customers worldwide, we're not just changing how people access financial products — we're creating a new credit system that backs the working, whatever they're working toward.
At Tilt, we’re constantly exploring new ways to understand financial behavior and reduce risk. The work you do in this role will help shape how we use foundational models across our products.
We’re looking for a Staff Data Scientist to join our Methodologies group — a role focused on building and advancing foundational modeling approaches. You’ll explore how deep learning methods such as neural networks, embeddings, and transformers can enhance how we understand financial data and make lending decisions.
This is a great role for someone who’s deeply curious about new modeling techniques and wants to see their ideas make a real difference. You’ll work with a small, experienced team of data scientists and engineers to design and apply deep learning methods to financial data challenges — from credit decisioning to transaction modeling.
You'll also be at the forefront of how data science is practiced in the age of AI agents. Our team builds with agentic tools every day, and we're creating specialized agents that speed up both DS and business workflows.
We’re looking for someone who enjoys learning, experimenting, and collaborating — a person who can connect technical exploration to meaningful business impact. If you’re excited by applying new methodologies in ways that help people access fairer credit, you’ll fit right in.
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.
Use agentic development tools (e.g., Claude Code, Codex) to speed up experimentation, feature engineering and model iteration.
Design and ship specialized agents that support DS and business workflows - these agents turn methodology into leverage across the company.
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.
Bonus: hands-on experience building with LLMs or agent frameworks, or excitement to learn. You see agents as a way to multiply what a small, senior team can do.
Don’t meet every qualification? We care about potential over your past. If you're bringing ambition and drive to what we're building, we want to hear from you.
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.
We're looking for people who chase excellence and impact. Those who stand behind their work, celebrating the wins and learning from the missteps equally. We foster an environment where every voice is valued and mutual respect is non-negotiable — brilliant jerks need not apply. We're in this together, working to expand access to fair credit and prove that people are incredible. When you join us, it's not just another day at the [virtual] office, you're helping millions of hardworking people reach better financial futures.
You’re pushing ahead in your career? We can get behind that. Join us in building the credit system that people deserve.
United States
Ukraine