What youâll be working on:
As a Staff ML Scientist, youâll be the most senior Individual Contributor (IC) Machine Learning Scientist across the entire FinCrime collective! This will give you a real opportunity to lead us into an exciting new phase of fraud and financial crime prevention, utilising billions of rows of data and the learnings from your previous successes in designing and building advanced Machine Learning based real time detection systems.
Weâre talking about Deep Learning, Graph neural networks, transformers â youâll have space to design the architecture that will help us take our real time detection systems to the next level.
More specifically, weâll be expecting you to leverage your deep experience of developing and deploying advanced Machine Learning models within the fields of financial crime, fraud, security, or trust and safety to:
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Lead our ongoing journey to build an advanced, scalable, extensible, automated fraud and FinCrime detection system that effectively prevents crime while minimising impact to genuine customers and operational costs.
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Ensure our detection systems can adapt quickly and appropriately to changing fraud and financial crime trends, remaining performant through time.
The technical approaches you take to solve these problems will be very much in your hands and weâll strongly encourage and support experimentation and innovation. Weâll be expecting you to justify and demonstrate effectiveness along the way, making sure the approach meets our business and customer needs.
As our most senior technical IC, youâll be providing key technical leadership and shipping highly impactful ML-based solutions. Youâll be empowered to work across the FinCrime collective identifying the most impactful areas and leading solution development.
Youâll work with our mission-oriented cross functional product squads, collaborating closely with product managers, data scientists, backend engineers and designers in an agile environment.
Youâll be expected to use your technical expertise to advise senior business stakeholders and help to set and advance our strategic direction in FinCrime.
Youâll also be a technical leader within the Machine Learning discipline, helping to steer technical work and drive up standards.
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Working with stakeholders across the organisation to identify and scope out the most impactful opportunities to tackle Financial Crime and Fraud with Machine Learning.
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Bringing the learnings from your previous successes in designing and building advanced Machine Learning based real time detection systems to lead advancements in our Financial Crime and Fraud detection capabilities, for example utilising deep learning, graph-based, and sequence-based architectures.
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Providing technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline, leading by example and mentoring others.
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Working closely with our MLOps team to steer the ongoing development of tools to enable rapid iteration of models and optimisations of the full ML model lifecycle.