What you'll be doing

Developing efficient and effective AML detection controls using unsupervised, semi-supervised, and supervised learning with GenAI
Creating frameworks to prove controls coverage at a regional level
Developing technologies to serve Wise’s diverse international user base
Working across functions to own and solve AML related problems at a global scale
Mentoring junior team members on technical and non-technical skills
Evaluating AML systems against internal and external benchmarks
Developing decisioning layers to optimize trade-offs between precision and recall
Providing data-driven insights on potential outcomes under various scenarios
Collaborating with operational teams to refine processes and integrate feedback into automation systems
Designing and managing projects utilizing excess operational capacity, such as manual data labeling for model improvement
Creating systems that provide in-depth insight to investigators on red flags and typologies in profiles/transactions
Packaging algorithms into deployable libraries and transitioning them from staging to production environments
Implementing and maintaining scheduled data gathering and model retraining pipelines
Maintaining production-grade Python services

Requirements

Experience implementing, training, testing, and evaluating machine learning systems
Strong Python knowledge, with proven familiarity and experience with OOP principles being a big plus
Experience with statistical analysis and designing well-structured experiments
Strong product mindset with ability to work independently in cross-functional environments
Good communication skills to effectively convey ideas to non-technical audiences
Strong problem solving skills to refine problem statements and determine solutions

Familiarity with automating operational processes using technical solutions like Large Language Models
Willingness to engage closely with operational teams to understand their pain points
Knowledge and experience within the Financial Crime domain