Wise
Lead Data Scientist - Trust and Safety
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Lead Data Scientist - Trust and Safety
Lead Data Scientist (Trust & Safety) – London
Company Description
Wise is a global technology company, building the best way to move and manage the world’s money. Our mission?
✅ Min fees. Max ease. Full speed. 🌍 Whether you’re sending money abroad, spending internationally, or managing international payments, Wise exists to make money moves seamless—while saving you money.
Join us in creating an entirely new network for the world’s money. For everyone, everywhere.
Job Description
We’re looking for a Lead Data Scientist to join our growing Trust & Safety Team in London.
This is a unique opportunity to work behind the scenes of company transactions—understanding fraud risks while ensuring our customers experience seamless, secure service every time.
Your impact? Direct. Your scale? Millions of Wise users.
The Role
As a Lead Data Scientist in Trust & Safety, you’ll:
- Innovate to stop fraudulent activities before they escalate.
- Strengthen our security framework and prevent unauthorized access.
- Collaborate with cross-functional teams—engineering, product, and security operations—to build data-driven fraud detection.
Key Responsibilities
- Lead development and deployment of advanced ML models to detect, predict, and mitigate account takeover (ATO) attempts and Send Scam frauds.
- Analyze large datasets to identify trends, anomalies, and risks tied to potential threats.
- Design and run experiments to measure fraud detection system performance and optimize continuously.
- Collaborate with security teams to translate risks into actionable insights and solutions.
- Develop scalable pipelines, algorithms, and real-time tools for fraud prevention.
- Stay updated on cutting-edge fraud detection techniques (e.g., ML, deep learning, graph solutions).
- Mentor junior data scientists, fostering a collaborative culture of growth.
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Requirements
Essential Qualifications
✔ Proven data science expertise in fraud detection, cybersecurity, or fintech. ✔ Built machine learning models specifically for Send Scam victim identification and account takeover (ATO) protection. ✔ Proficient in Python (or similar) for ML development. ✔ Experience with large datasets and technologies like Hadoop, Spark, or BigQuery. ✔ Knowledge of:
- Anomaly detection methods
- Supervised/unsupervised learning
- Deep learning and graph-based solutions for fraud analysis ✔ Strong collaboration with cross-functional teams and stakeholder communication skills. ✔ A problem-solving mindset and passion for recognizing and stopping fraud.
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Nice-to-Haves
🔹 Experience working with event logs to mine patterns and associations. 🔹 Familiarity with various model types (gradient boosting, neural networks, autoencoders, clustering). 🔹 Statistical analysis skills and clear presentation ability to convert insights into action. 🔹 A strong product mindset with cross-team agility. 🔹 Git collaboration and code review experience. 🔹 Java knowledge (helpful when working with engineering teams).
Additional Info & Culture
Why WISE?
✅ No Boarders – We value people without prejudice. Your skills matter more than a degree. ✅ Diversity drives excellence. We actively seek under-represented voices to build stronger teams. ✅ Inclusivity first. We celebrate differences and ensure every Wise teammate feels respected, heard, and empowered. 🌍 Our international team moves fast—where inclusion fuels innovation.
👉 Learn more about working at Wise!
Stay connected: 📧 LinkedIn | 📸 Instagram
Compensation
💰 GBP 90,500 — GBP 127,000 per year
Want to help shape the future of money flows? Respond with your expertise and passion for fraud prevention, clarity, and collaboration—it’s all we need. ⚡
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