Lead Data Scientist

AEON Bank; Kuala Lumpur; On-site; MYR 240,000 - 420,000; Full time

Job summary

AEON Bank is seeking a Lead Data Scientist to architect intelligent, data-driven banking solutions and lead a high-performing team delivering production-scale AI/ML that directly impacts customers. You will drive the next generation of data-powered decisions with strong governance and explainability.

Collaborate with product and business stakeholders across Finance, Risk, AML and Marketing to shape AI strategy, mentor junior data scientists, and ensure reproducible, scalable models aligned with

Qualifications

• 7+ years in Data Science and ML with leadership experience.
• Strong Python and SQL proficiency for production models.
• Experience delivering explainable AI and governance.
• Familiarity with LLM/GenAI workflows and MLOps.

Responsibilities

• Lead a team to design, train, and productionize AI/ML models.
• Collaborate with product and business stakeholders across domains.
• Establish model governance, observability, and explainability frameworks.
• Drive AI strategy, roadmap, and cross-functional initiatives.
• Mentor junior data scientists and promote reproducible code.

Skills

Python

SQL

Statistics

Machine Learning

LLM/GenAI

Observability

Leadership

Education

Bachelor's degree in Data Science or related field

Tools

TensorFlow

PyTorch

MLflow

Airflow

Snowflake

AWS

Google Cloud

Job description

Join Aeon Bank at the forefront of the digital banking evolution. We are seeking an accomplished Lead Data Scientist to help us architect the next generation of intelligent, data-driven banking solutions. In this pivotal role, you will lead a high-performing team to turn rich financial data into powerful, production-scale AI/ML solutions that directly impact our customers' lives. If you are passionate about driving measurable business value, advancing cutting-edge AI, and fostering a culture of technical excellence within a dynamic, innovation-first environment, we want to hear from you.

As a technical lead, you will champion our AI efforts, delivering production-scale machine learning solutions that drive measurable business value while adhering to the highest standards of regulatory explainability.

Job Responsibilities

• Work closely with data leads to shape the bank’s AI strategy and roadmap, and lead a talented team to drive its successful execution.
• Collaborate with product and business stakeholders to drive cross-functional AI initiatives across diverse banking domains—including Finance, Risk, AML and Marketing.
• Oversee end-to-end ML model training, integrating robust observability, evaluation and explainability frameworks to ensure high-performing, transparent, and auditable models.
• Collaborate with the Machine Learning Engineering team to establish industry-leading model tracking, lifecycle management and engineering best practices.
• Orchestrate strategic plans involving Large Language Models and Generative AI across the organisation, e.g. Identify and implement AI-driven workflows for business processes and decisioning logic, while ensuring robust governance and human-in-the-loop oversight.
• Explain complex technical ideas in simple, clear terms, making them easy to understand for everyone from engineering peers to senior leadership.
• Cultivate a culture of excellence and ownership by leading and mentoring junior data scientists. As a team lead, you will also set the standard for reproducible, production-grade data science code and methodology.

Job Requirements

• Bachelor’s degree or higher in Data Science, Computer Science or a related quantitative field.
• 7+ years of experience in Data Science and Machine Learning, with a track record of leading and mentoring high-performing teams to successfully design, deploy, and scale machine learning models into production environments.
• Proven problem-solving mindset with the ability to bridge the gap between complex business requirements and technical data science solutions.
• Strong proficiency in Python and SQL to manipulate complex datasets, conduct deep statistical analysis, and engineer robust, production-ready predictive models.
• Strong foundation in statistics, predictive modelling, machine learning algorithm and model evaluation techniques.
• Deep expertise in Agentic AI architectures, including memory management, tool integration, and observability, with proven practical experience in architecting LLM/GenAI workflows.
• Hands-on experience with cloud-native platforms (AWS, Google Cloud, Snowflake) and MLOps orchestration (MLflow, Airflow) to develop scalable model training pipelines.
• Demonstrated experience training models with frameworks such as TensorFlow or PyTorch is a plus.
• Excellent analytical thinking, communication and stakeholder management skills, with ability to influence business stakeholders and lead cross-functional initiatives.