Senior ML Engineer

About this role

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Skills you'll use

What you'll do

• Design, build, and continuously improve the ML models that power our risk and intelligence products, and take ownership of new signals as they get scoped.
• Take a loosely defined business or customer problem and break it into a clear data problem, articulating value, impact, and complexity before proposing a solution.
• Build anomaly detection, fraud, and risk-modeling approaches — including network/graph-based methods — that keep our signals accurate and resistant to adversarial behaviour.
• Own model development, deployment, and monitoring end-to-end, partnering with ML/data engineers on scalability, reliability, cost, and dashboards/alerting.
• Design and run experiments (A/B tests, offline customer POCs) to validate new signals before they roll into production.
• Manage and analyse large, multi-country datasets, ensuring data integrity, consistency, and compliance throughout.
• Partner cross-functionally with Product, Engineering, Legal, and GTM/Sales to scope, prioritise, and ship data products on time, acting as a trusted advisor on what the data can and can't responsibly support.

Who can apply

• Manage and analyse large, multi-country datasets, ensuring data integrity, consistency, and compliance throughout.
• Partner cross-functionally with Product, Engineering, Legal, and GTM/Sales to scope, prioritise, and ship data products on time, acting as a trusted advisor on what the data can and can't responsibly support.
• 5+ years of experience designing, building, and deploying ML models at scale, ideally including risk/fraud, propensity, or behavioural/network scoring use cases.
• Strong grounding in applied machine learning: classification, anomaly detection, propensity/scoring models, clustering, and time-series/drift monitoring.
• Exposure to graph-based analysis or graph ML (network embeddings, community detection, link prediction) is a plus.
• Hands-on experience taking models from research/experimentation into production — comfortable owning scalability, reliability, and monitoring, not just model accuracy.
• Working knowledge of NLP and LLM-based techniques (prompting, summarisation, fine-tuning) — useful for customer-facing AI insights and on-device text/SMS signal extraction.
• Proficiency in Python and the ML/data stack: Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch; comfortable with Hugging Face Transformers where relevant.

Perks & benefits

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