한국어·English
Airwallex company · OPEN ROLE
Staff Data Scientist, Algorithm (Risk Product - AML & Financial Crime)
Singapore · Engineering · AI
- Company
- Supports businesses' cross-border payments, currency exchange, and card issuing on one platform.
- Role
- Develops AI detection features that catch financial crime before it happens.
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What you will do
We rewrote the responsibilities and qualifications in plain language instead of copying the original posting.
Responsibilities
- Leads the data science approach for anti money laundering and financial crime controls across Risk Product.
- Digs into customer, transaction, behavioral, network, and external data to surface new threats, odd behavior, control gaps, and crime patterns nobody has seen yet.
- Grows advanced risk capabilities that rely on AI powered screening, graph and network analysis, entity matching, and machine learning.
- Pushes forward core work such as name screening, transaction monitoring, and spotting coordinated fraud or financial crime rings.
- Works closely with the Risk Product, Engineering, Compliance, and Operations groups that handle financial crime, translating thorny problems into controls that actually run in production.
- Shapes the risk data science roadmap, builds methods others can reuse, and mentors teammates.
What you need
- Brings 7+ years of hands on work spanning data science, applied machine learning, AI, or quantitative risk analysis, with real staff level scope behind it.
- Has built and shipped machine learning solutions that run on real data in production.
- Is skilled in at least one of NLP or large language models, entity matching, information retrieval, graph analysis, anomaly detection, or risk scoring.
- Is strong in SQL and Python.
- Can talk with and bring around senior people spanning product, risk, engineering, compliance, and operations.
Good to have
- Comes from a regulated or high stakes world such as fintech, payments, banking, or marketplaces.
- Has built or upgraded name screening or adverse media systems for anti money laundering, sanctions, fraud, or monitoring work.
- Is comfortable with evaluating large language models, running prompt or model experiments, checking retrieval quality, and AI observability.
- Has used graph analysis to catch mule networks, collusion, or coordinated account activity.
- Has built proactive risk discovery tools rather than only tuning existing supervised models or rules.
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Checked 2026-10-19
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