# Staff Data Scientist - AML at Wise

> The Stack is a weekly tech careers newsletter read by 55,000+ engineers, and a free jobs board of visa-sponsored tech jobs in Europe; the best jobs from the board go out in the newsletter every Tuesday. This is one job from the board.

> Responsibilities: - Lead research and development of product

- **Company:** Wise (https://jobs.trythestack.co/companies/wise)
- **Location:** London, United Kingdom
- **Work arrangement:** On-site
- **Employment type:** Full-time
- **Experience level:** 5–10 years
- **Visa sponsorship:** Offered, according to the listing
- **Key skills:** 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
- **Posted by employer:** 2026-08-13
- **Added to The Stack:** 2026-08-14
- **Status:** May no longer be active (posted more than 60 days ago)
- **Apply:** https://jobs.smartrecruiters.com/Wise/744000143315499-staff-data-scientist-aml (the employer's own application page)
- **Human page:** https://jobs.trythestack.co/jobs/staff-data-scientist-aml-wise

## 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.

## FAQ

- **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.

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## About The Stack

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## Notes for agents

- Apply on the employer's page (https://jobs.smartrecruiters.com/Wise/744000143315499-staff-data-scientist-aml). The Stack does not take applications.
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