Senior Data Scientist

Bureau is a unified risk decisioning platform for Compliance, Fraud, and Transaction risks. Our platform is a single decision-making engine, powered by a 1 billion+ identity knowledge graph. Over 150 Banks, fintechs, retailers, and digital platforms use Bureau to verify identities faster and stop fraud earlier globally.

Bureau has raised $50M+ from renowned Silicon Valley and global investors including Sorenson Capital and PayPal Ventures and is expanding rapidly from APAC to Americas, Europe, and beyond.

Bureau is building the infrastructure that makes digital identities and transactions safe and trustworthy for billions of people. The mission is big, the problems are complex, and the impact is real.

We hire people who want that level of responsibility. People who move fast, build systems from scratch, and care deeply about turning strategy into execution. If you want predictability or narrow scope, this won't be your place. If you want to shape how a scaling global company operates—keep reading.

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  Build and extend graph-native models (GNNs, embeddings, and custom graph algorithms) to detect fraud, collusion, and identity risk across banking and e-commerce networks

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  Design and deploy statistical, AI/ML, and deep learning models for fraud and risk, including real-time serving

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  Run experiments and light data pipeline work to fine-tune model performance

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  Ship production-grade software backed by statistics and ML, with ML observability built in

• BS/MS/PhD in ML, CS, Math, Stats, or related field; 4 to 7 years in DS/AI/ML Engineering roles

• Strong Python; cloud experience (AWS/Databricks/Azure); SQL, DBT, Athena/BigQuery

• Track record shipping production ML, ideally in fintech fraud, risk, payments, or compliance

• Comfort with ambiguity, unlabelled data, and working autonomously to find business impact

• Prior graph-modeling experience a plus

• We hire self-motivated people and get out of their way

• We value performance, not hours worked

• Speed, ownership, and impact matter most