Staff Data Scientist - AML at Wise

✨ The Stack take on this Wise job

• 🏢 Wise has 47 jobs here, 45 with visa sponsorship see all
• 🛂 Skilled Worker visa. Standard minimum salary: £41,700 a year guide
• 💡 No salary shown. Ask Wise if it is above the minimum.

Updated 2026-11-03, from our data and official sources.

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About the role

Responsibilities:
- Lead research and development of production ML models for AML: anomaly detection, graph models, neural networks and Transformer-based systems.
- Design modular, evidenceable detection pipelines that surface typologies and red flags across regions.
- Collaborate with AML investigators, product and platform teams to embed models into real-time monitoring and investigation workflows.
- Define evaluation metrics, validation strategies and explainability to meet regulatory and audit requirements.
- Build scalable deployment and monitoring patterns with platform engineers; own model lifecycle (training, testing, serving, monitoring).
- Mentor data scientists and promote best practices in modelling, reproducibility and data hygiene.

Requirements:
- 5+ years building production-grade ML systems, preferably in AML, fraud, risk or financial crime domains.
- Strong Python skills and deep learning experience with frameworks such as TensorFlow or PyTorch.
- Practical experience with graph-based models, anomaly detection methods and Transformer architectures.
- Familiarity with LLM orchestration and tools (LLamaIndex, LangGraph) is desirable for automation/augmentation tasks.
- Experience deploying models at scale, monitoring model performance and addressing concept drift.
- Excellent communication skills and ability to work with investigators, engineers and product managers to deliver practical solutions.
- Strong quantitative mindset, reproducible workflows and knowledge of big-data tooling/environments.

Responsibilities

Lead the development and deployment of advanced machine learning models to enhance AML detection capabilities and regional typology understanding. Collaborate with cross-functional teams to integrate data science solutions and mentor team members on technical innovation.

Requirements

Requires 5+ years of experience in developing production-grade AI and machine learning systems within financial risk or fraud domains. Candidates must possess strong technical proficiency in Python, deep learning frameworks, and LLM orchestration tools.

Key skills

• Python — in 1173 other listings on The Stack
• Machine learning — in 285 other listings on The Stack
• Neural networks — in 6 other listings on The Stack
• Deep learning — in 49 other listings on The Stack
• TensorFlow — in 55 other listings on The Stack
• PyTorch — in 90 other listings on The Stack
• LLM orchestration — in 8 other listings on The Stack
• LLamaIndex — in 5 other listings on The Stack
• LangGraph
• Anomaly detection
• Graph-based models
• Data strategy
• Big-data frameworks
• Technical leadership
• Mentorship
• Financial risk

On The Stack (as of 2026-11-03)

• Wise has 46 other listings on The Stack, 44 of them marked as offering visa sponsorship.
• 209 visa-sponsored jobs in the United Kingdom on The Stack, posted in the last 60 days.
• 65 visa-sponsored data scientist jobs in Europe on The Stack, posted in the last 60 days.

Frequently asked questions about Wise

Is this Staff Data Scientist - AML role still open?

It was posted on 2026-08-13. Listings more than 30 days old may no longer be active, so check Wise's site before applying.

Does Wise offer visa sponsorship?

This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Wise's site.

Where is this role located?

The listing gives the location as London, United Kingdom. It is marked on-site.

What skills does this role ask for?

The listing names: Python, Machine learning, Neural networks, Deep learning, TensorFlow, PyTorch, LLM orchestration, LLamaIndex, LangGraph, Anomaly detection, Graph-based models, Data strategy, Big-data frameworks, Technical leadership, Mentorship, Financial risk.