Pay
$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Remote - US · Remote - EU · United States
Actively hiring, first seen by us 7 days agoLocation type: RemoteFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: Credit › Data ScienceFrom the employer’s posting
Develop and test new modeling approaches using deep learning architectures
Build and refine frameworks that integrate into existing ML pipelines for credit and loss prediction
Experiment with pretraining and fine-tuning on real financial data
Share findings and mentor others to grow the team's deep learning capability
Design and ship specialized agents that support DS and business workflows
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.
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.
Design and ship specialized agents that support DS and business workflows
Actively hiringFirst seen by us 7 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Day 7
Similar jobs usually come down between day 7 and day 55.
From 10,000 similar postings that came down. Coming down is not always a hire.
3 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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).
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We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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.
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United States
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