Sr. Data Scientist

Cygnify; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

Cygnify in Singapore is 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.

You will build and deploy ML models for credit risk, payments, and fraud detection, analyze transactional data, perform feature engineering, and ensure models comply with regulatory standards.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field.
• Proven experience in data science, preferably within banking, fintech, or fraud analytics.
• Strong proficiency in Python, SQL, and machine learning techniques.
• Hands-on experience with NLP techniques.
• Familiarity with Generative AI or 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

Python

SQL

Machine Learning

NLP

Generative AI/LLMs

Banking knowledge

Fraud analytics

Risk modeling

Education

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

Tools

Python

SQL

Job description

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.
• Proven experience in data science, preferably within banking, fintech, or fraud analytics.
• Strong proficiency in Python, SQL, and machine learning techniques.
• Hands-on experience with NLP techniques.
• Familiarity with Generative AI or 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.