AI Scientist - Anti-Fraud (Junior)

DUOTECH PTE. LTD.; Singapore; On-site; SGD 103,000 - 156,000; Full time

Job summary

A fintech company in Singapore seeks experienced AI Scientists to build fraud detection systems using advanced machine learning techniques. The role involves collaborating with various teams to implement scalable systems for anomaly detection and prevention in real-time. Candidates should have a degree in Computer Science/Data Science, strong ML experience, and proficiency in both Chinese and English.

Qualifications

• 2+ years of hands-on experience in machine learning and fraud detection, ideally in fintech, e-commerce, or Web3.
• Proficiency in Python and ML frameworks like PyTorch or TensorFlow.
• Solid understanding of EVM-compatible blockchain data and wallet behaviors.

Responsibilities

• Design, build, and deploy AI models using transformer architectures for fraud detection.
• Build scalable graph learning pipelines to detect malicious actor communities.
• Implement high-performance fraud scoring systems to operate at scale.
• Support fraud investigation workflows with actionable insights.
• Stay updated on emerging fraud threats and develop proactive solutions.

Skills

Graph machine learning

GNNs

Transformer models

Fraud detection techniques

Feature engineering

Python

PyTorch/TensorFlow

Big data processing tools

Real-time data pipelines

EVM-compatible blockchain data

Chinese and English proficiency

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, or related field

Tools

Spark

Hive

Kafka

Flink

Kafka

Flink

Job description

We are seeking multiple experienced AI Scientists to build intelligent fraud detection and abuse prevention systems that secure our users and ecosystem. You will leverage the latest advancements in graph ML, transformers, and behavioral analytics to address some of the most critical threats today.

Key Risk Use Cases You'll Tackle

• Identity Risk: Fake KYC profiles, identity farming, synthetic user creation
• Account Takeover (ATO): Compromised credentials, anomalous login behavior, device takeover
• Toll Fraud & Drainers: Gas draining contracts, malicious token airdrops, phishing‑induced approval scams
• Money Laundering: Cross‑chain obfuscation, mixer usage, layering and integration strategies
• Campaign Reward Abuse (Treaty Hunters): Multi‑account farming, airdrop exploitation, referral & cashback fraud across marketing programs

You’ll work closely with engineering, product, compliance, and security teams to build end‑to‑end machine learning systems that detect, explain, and prevent these behaviors in real time.

Responsibilities

• Design, build, and deploy AI models using transformer architectures, GNNs, and unsupervised learning for anomaly and abuse detection.
• Build scalable graph learning pipelines to detect communities of malicious actors and their on‑chain behavioral patterns.
• Create robust behavioral feature extraction layers using on‑chain transaction data, login events, referral logs, device/browser/session metadata.
• Implement high‑performance fraud scoring systems that can operate in real time at massive scale.
• Collaborate with campaign & marketing teams to identify abuse vectors in rewards, referral, and incentive systems.
• Conduct regular performance evaluations of ML models and continuously improve them using new research and adversarial learning techniques.
• Support fraud investigation workflows by providing interpretable model results and actionable insights.
• Stay current on the evolving threat landscape and proactively develop solutions against novel fraud patterns.

Qualifications

Must‑Have

• Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.
• 2+ years of hands‑on experience in machine learning and fraud detection, ideally in fintech, e‑commerce, or Web3.
• Strong background in:
• Graph machine learning, GNNs, and clustering algorithms
• Transformer models and attention mechanisms
• Fraud detection techniques, including anomaly detection and behavioral scoring
• Feature engineering from high‑dimensional, noisy, and heterogeneous data sources
• Proficiency in Python and ML frameworks like PyTorch/TensorFlow.
• Familiarity with big data processing tools (Spark, Hive) and real‑time data pipelines (Kafka, Flink).
• Solid understanding of EVM‑compatible blockchain data (transactions, contract calls, logs) and wallet behaviors.
• Able to use Chinese and English as working language to work with Chinese speaking stakeholders

Preferred

• Experience with fraud rule engines or security scoring systems.
• Familiarity with tools like Chainalysis, Tenderly, Alchemy, or similar blockchain analytics platforms.
• Experience modeling campaign abuse behaviors, e.g., multi‑account ring detection, bonus hunting, or engagement spoofing.