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Data Scientist – AI Fintech

Cygnify

Singapore · On-site Full-time 1mo ago

About the role

Role: Data Scientist (Machine Learning) – AI Fintech Location: Singapore

We are partnering with a leading AI Fintech seeking a Data Scientist with expertise in banking models, fraud analytics, and machine learning. You will design predictive models, detect fraud patterns, and collaborate with Risk, Product, and Engineering teams to deliver data-driven solutions.

Responsibilities: • Build and deploy machine learning models for credit risk, payments, and fraud detection.

• Analyze transactional data to identify fraud patterns and emerging risks.

• Perform feature engineering, model training, validation, and monitoring.

• Collaborate with cross-functional teams to implement models into production systems.

• Ensure all models meet regulatory and compliance standards.

Requirements: • Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field.

• 3+ years of experience in data science, ideally with a focus on banking or fraud analytics.

• Strong skills in Python, SQL, and machine learning tools; experience with GenAI and LLMs is a plus.

• Solid understanding of banking products, fraud schemes, and risk modeling techniques.

• Hands-on experience with supervised and unsupervised machine learning, applied statistics, and AI methods.

#LI-SC1

Requirements

  • Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field.
  • 3+ years of experience in data science, ideally with a focus on banking or fraud analytics.
  • Strong skills in Python, SQL, and machine learning tools; experience with GenAI and LLMs is a plus.
  • Solid understanding of banking products, fraud schemes, and risk modeling techniques.
  • Hands-on experience with supervised and unsupervised machine learning, applied statistics, and AI methods.

Responsibilities

  • Build and deploy machine learning models for credit risk, payments, and fraud detection.
  • Analyze transactional data to identify fraud patterns and emerging risks.
  • Perform feature engineering, model training, validation, and monitoring.
  • Collaborate with cross-functional teams to implement models into production systems.
  • Ensure all models meet regulatory and compliance standards.

Skills

PythonSQLMachine LearningGenAILLMsData ScienceStatisticsComputer ScienceBanking ProductsFraud SchemesRisk Modeling

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