Risk Data Scientist - Credit

Monee; Singapore; Sur place; SGD 60 000 - 90 000; Plein temps

Résumé du poste

A financial technology company in Singapore is looking for a Risk Data Scientist - Credit to develop end-to-end AI models supporting risk processes in lending. The ideal candidate should hold at least a Bachelor's degree in a quantitative field, have proficiency in Python and SQL, and experience with machine learning frameworks. Collaboration and strong communication skills are essential. This role offers opportunities for both entry-level and experienced candidates.

Qualifications

• Bachelor’s or Master’s degree in a quantitative field required.
• Experience in credit risk or anti-fraud modeling preferred.
• Strong communication and collaboration skills.

Responsabilités

• Build end-to-end AI models for key risk processes.
• Perform data mining and feature engineering.
• Manage the full model lifecycle.
• Analyze model performance and extract insights.
• Explore advanced modeling techniques.

Connaissances

Proficient in Python

Strong SQL skills

Hands-on experience in applied machine learning

Experience with ML/DL frameworks (scikit-learn, PyTorch)

Collaboration skills

Formation

Bachelor’s degree in Computer Science or related field

Master’s degree preferred

Outils

Hadoop

Spark

Description du poste

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Job Description

• Build end-to-end AI models (statistical, machine learning, deep learning, LLM) to support key risk processes across retail and SME lending, including fraud detection, risk assessment, underwriting, and product/limit optimization.
• Perform data mining and feature engineering using large-scale transactional, behavioral, and financial data, ensuring high-quality datasets for modeling.
• Manage the full model lifecycle: development, deployment, production support, and performance monitoring.
• Analyze model performance and extract data insights to guide decision-making and strategy.
• Explore advanced modeling techniques (eg, sequential models, graph learning, uplift modeling, causal inference) to solve complex business challenges and improve profitability and risk control.
• Collaborate closely with Strategy, Business, and Engineering teams to translate business problems into modeling solutions and drive implementation.

Requirements

• Bachelor’s degree or above in Computer Science, Mathematics, Statistics, Quantitative Finance, or related field; Master’s degree preferred.
• Open to candidates across experience levels, including entry-level and experienced professionals.
• Hands‑on internship or project experience in applied machine learning or data science; experience in credit risk or anti‑fraud modeling is a plus.
• Proficient in Python with experience in ML/DL frameworks such as scikit‑learn and PyTorch.
• Strong SQL skills; experience with big‑data tools (eg, Hadoop, Spark) is highly preferred.
• Solid understanding of machine learning algorithms; expertise in at least one of the following areas is a plus: sequential modeling, graph learning, causal inference, multi‑task learning, reinforcement learning, transfer learning.
• Self‑driven, proactive, positive mindset, and strong communication and collaboration skills.

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