Expert Algorithm Engineer - Credit

About the team

We are looking for an Expert Algorithm Engineer to lead a team in developing next-generation behavioural sequence modelling and transaction foundation models for credit risk management.

This is a hands-on technical leadership role suited for someone with strong expertise in deep learning, sequence modelling, Transformer architectures, and large-scale model experimentation, combined with practical exposure to credit or financial risk modelling.

The role offers the opportunity to build advanced user behavioural representation models and apply them to real-world risk decisioning at scale.

What you'll do

• Lead the development of large-scale behavioural sequence models and user representation frameworks for credit and financial risk applications.
• Build behavioural foundation models that support risk modelling, decisioning, and portfolio management at scale.
• Apply advanced modelling techniques, including Transformer architectures, self-supervised learning, representation learning, and structured behavioural pretraining.
• Define the modelling roadmap, including architecture design, training objectives, experimentation strategy, and evaluation framework.
• Conduct large-scale experiments to improve model performance, stability, scalability, reproducibility, and downstream effectiveness.
• Partner with Data Engineering and ML Engineering teams to productionize model pipelines, serving, monitoring, and cross-market deployment.
• Lead and mentor a team of Data Scientists / Algorithm Engineers, while working closely with cross-functional stakeholders to drive model development, deployment, and adoption in real-world risk decisioning.

Requirements

• Bachelor’s degree or above in Computer Science, Statistics, Mathematics, Data Science, Engineering, Machine Learning, Artificial Intelligence, or a related discipline. Master’s or PhD degree is preferred.
• Minimum 5 years of experience in machine learning, deep learning, sequence modelling, representation learning, credit risk modelling, fraud modelling, recommendation systems, NLP, or related areas.
• Strong hands-on experience with Python, SQL, PyTorch or TensorFlow, and large-scale data processing frameworks such as Spark, Hive, Hadoop, Flink, or equivalent platforms.
• Solid understanding of sequence modelling, Transformer architectures, self-supervised learning, pretraining, representation learning, embedding models, and foundation model approaches.
• Experience working with large-scale behavioural, transaction, event-level, user journey, or time-series datasets.
• Strong ability to design experiments, conduct ablation studies, debug training issues, evaluate model performance, and apply sound validation practices.
• Familiarity with production ML systems, including feature pipelines, model deployment, monitoring, experiment tracking, model governance, and scalable model serving.
• Proven ability to lead technical projects from research exploration to production deployment, mentor junior team members, and collaborate effectively with cross-functional stakeholders.
• Strong ownership, problem-solving ability, and English communication skills, with the ability to explain complex modelling concepts clearly.