Lead Data Scientist, MethodologiesTilt (formerly Empower) • London, England, United Kingdom
Lead Data Scientist, Methodologies
Tilt (formerly Empower)
- London, England, United Kingdom
- London, England, United Kingdom
Über
Join the Tilt team! At Tilt, we use mobile-first products and machine-learning powered credit models to look beyond outdated credit scores, using over 250 real-time financial signals to recognize real potential. 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.
The Opportunity - Lead Data Scientist, Methodologies At Tilt, we constantly explore new ways to understand financial behavior and reduce risk. In this role you’ll shape how we use foundational models across our products. You’ll build and advance foundational modeling approaches, applying deep‑learning methods such as neural networks, embeddings, and transformers to enhance how we understand financial data and make lending decisions. This is a great role for someone who is 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 challenges from credit decisioning to transaction modeling. Tilt is a remote‑first company that fosters connectivity through regular off‑sites; travel for company off‑sites is required at least twice per year.
How You’ll Make An Impact
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 pre‑training 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.
Why You’re a Great Fit
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.
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.
What You’ll Get At Tilt
Virtual‑first teamwork – the Tilt team collaborates across 14 countries, 12 time zones, and counting. You’ll start with a WFH office reimbursement.
Competitive pay – we reward potential, reflected in competitive compensation packages and generous equity.
Complete support – flexible health plans at every premium level, with substantial subsidies meeting global standards.
Visibility – direct exposure to leadership, where good ideas travel quickly.
Paid global on‑sites – we gather twice yearly for shared meals or kayaking adventures (Vail, San Diego, Mexico City). Impact is recognized – growth opportunities follow your contributions, not rigid promotion timelines.
The Tilt Way We look for people who chase excellence and impact, who stand behind their work and celebrate wins while learning from 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 people are incredible. When you join us, it’s not just another day at the virtual office; you’re helping millions reach better financial futures.
You’re pushing ahead in your career? We can support that. Join us in building the credit system that people deserve.
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Sprachkenntnisse
- English
Hinweis für Nutzer
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