Risk Data Scientist - Credit

Role overview

Monee is looking for a Risk Data Scientist focused on credit to develop AI-driven solutions that strengthen risk decisioning across retail and SME lending. The role centers on building models that improve fraud detection, risk assessment, underwriting, and product or credit limit optimization.

What you will do

• Create full-stack AI and analytics models spanning statistical methods, machine learning, deep learning, and LLM-based approaches to solve core risk use cases.
• Work with large transaction, behavioral, and financial datasets to clean, mine, and engineer features for robust model inputs.
• Own the complete lifecycle of models, from experimentation and development through deployment, production support, and ongoing monitoring.
• Review model outcomes, identify patterns in the data, and turn those findings into actionable business recommendations.
• Investigate more advanced techniques such as sequence models, graph-based learning, uplift modeling, and causal inference to tackle difficult business problems and support better profitability and risk controls.
• Partner with Strategy, Business, and Engineering stakeholders to convert business needs into deployable modeling solutions and implementation plans.

What we are looking for

• A bachelor’s degree or higher in Computer Science, Mathematics, Statistics, Quantitative Finance, or a related discipline; a master’s degree is preferred.
• Practical exposure through internships or projects in applied machine learning or data science; prior work in credit risk or anti-fraud models will be an advantage.
• Strong Python skills, with hands-on experience using machine learning and deep learning libraries such as scikit-learn and PyTorch.
• Very good SQL ability, with prior use of large-scale data platforms like Hadoop or Spark preferred.
• Sound knowledge of machine learning methods, plus additional strength in one or more of these areas: sequential modeling, graph learning, causal inference, multi-task learning, reinforcement learning, or transfer learning.
• A self-motivated, proactive attitude with a collaborative approach and strong communication skills.

Additional information

This is a full-time onsite role based in Singapore, Singapore.