Job Description

Design and build scalable machine learning models using large-scale transaction, behavioral, financial, and credit bureau data to support retail and SME lending, covering underwriting, credit limit management, risk-based pricing, and portfolio optimization.
Extract actionable insights from complex datasets and apply both traditional statistical methods and advanced deep learning techniques to maximize model performance and business impact.
Partner closely with Strategy and Business teams to define problems, design modeling solutions, and convert insights into measurable outcomes.
Collaborate with Data Engineering to deploy models into production, enhance performance in live environments, and establish end-to-end monitoring and evaluation mechanisms.
Continuously explore state-of-the-art methodologies including sequential modeling, graph learning, causal inference, and multi-task learning to solve challenging risk-decision problems.
Lead AI/LLM-driven initiatives to enhance modeling efficiency, feature engineering, and advanced data mining capabilities.

Requirements

Master's degree or above in Computer Science, Mathematics, Statistics, Quantitative Finance, or related discipline.
Minimum 3 years of hands-on experience in applied machine learning or data science; experience in credit risk or anti-fraud modeling is strongly preferred.
Proficient in Python with solid experience using scikit-learn and deep learning frameworks such as PyTorch.
Familiar with big-data ecosystems and distributed computing (Hadoop/Spark/SQL).
Broad knowledge of machine learning algorithms with deep expertise in at least one area: sequential modeling, graph learning, causal inference, multi-task learning, or reinforcement learning.
Strong ownership, communication, and cross-functional collaboration skills; ability to independently drive complex modeling projects from ideation to production.

Data Science (Risk) Expert - Monee