AI/ML Engineer - Data Analytics (Banking)

Summary
You will drive advanced analytics and machine learning strategies within a major banking environment to unlock actionable business recommendations. Mining massive, multi-dimensional datasets, you will design and embed predictive models, generative AI frameworks, and LLM solutions that directly influence corporate strategy. This role gives you the ownership to translate complex commercial problems into scalable technical architectures, collaborating closely with cross-functional teams to measure real-world performance impact and elevate customer service delivery benchmarks.

Responsibilities

• Mine, analyze, and model large structured and unstructured data assets using advanced statistical methods and leading machine learning algorithms.
• Translate core business challenges into analytics-led solutions, manage model deployment, and rigorously evaluate commercial results.
• Anticipate evolving business priorities and state of play to ensure timely, high-quality deliverables within a dynamic environment.
• Collaborate across diverse teams to implement machine learning architectures with well-defined performance measurement frameworks.
• Share domain knowledge and technical best practices to cultivate a productive, supportive, and collaborative engineering ecosystem.
• Adhere strictly to institutional policies, operational procedures, and risk control requirements across daily workflows and special projects.

Requirement

• University degree in Statistics, Mathematics, Computer Science, Information Systems, or a related quantitative discipline.
• Minimum of 2 years of banking experience specialized in data analytics, statistical modeling, and machine learning/AI workflows.
• Deep technical knowledge of machine learning algorithms alongside proven expertise in processing massive data volumes for rapid insight discovery.
• Hands-on proficiency with Python, SQL/BigQuery, and Tableau/QlikSense within Google Cloud Platform (GCP) environments.
• Practical familiarity with embedding techniques, Retrieval-Augmented Generation (RAG), prompt engineering, and LLM fine-tuning.
• Strong communication, presentation, and stakeholder management skills, with the capacity to articulate complex technical architectures to non-technical business partners.