Technology Watch & Feasibility

• Conduct regular technology watch on AI advancements, including research papers, emerging techniques, and new tools
• Perform rapid technical feasibility assessments for business use cases within short timelines (2–3 days)
• Benchmark market solutions by comparing vendor offerings and open‑source alternatives using decision matrices
• Develop quick proof‑of‑concepts (POCs) to validate hypotheses prior to significant investment
• Provide clear, actionable technical recommendations to support decision‑making
• Participate in business workshops to understand objectives, constraints, and potential AI applications

AI Model Development

• Design and develop machine learning and deep learning models for prioritized business use cases
• Select and justify appropriate algorithms and technical approaches, from baseline to state‑of‑the‑art solutions
• Perform feature engineering and model optimization to enhance performance, robustness, and reliability
• Deliver rapid prototypes using vibe coding methodologies within 2–4 weeks
• Ensure rigorous model validation, including performance metrics, robustness checks, and bias assessment
• Produce comprehensive technical documentation covering methodology, experiments, and results
• Collaborate closely with AI developers to support solution industrialization and deployment

Mentoring & Knowledge Sharing

• Provide hands‑on support to junior Data Scientists through pair programming and code reviews
• Facilitate knowledge transfer on advanced AI techniques, tools, and best practices
• Share technology watch insights with the team via documentation, presentations, and technical talks
• Contribute to and evolve a shared library of prompts and vibe‑coding techniques
• Lead technical workshops and feedback sessions to promote continuous learning and improvement

• Bachelor’s or Master’s degree in Information Technology or an equivalent field
• Minimum of 7 years’ experience in Data Science and Machine Learning
• Proven experience within the banking or finance sector, with understanding of business and regulatory challenges
• Demonstrated expertise in vibe coding, leveraging AI tools for rapid prototyping and productivity
• Strong expertise across ML/DL techniques, including:
  • Supervised and unsupervised learning
  • Deep learning, NLP, and time‑series analysis
• Proficiency in Python and key ML frameworks:
  • scikit‑learn, TensorFlow, PyTorch, XGBoost, LightGBM
• In‑depth knowledge of Large Language Models (LLMs) and modern approaches:
  • Fine‑tuning, Retrieval‑Augmented Generation (RAG), prompt engineering, and agents
• Advanced skills in feature engineering and variable selection
• Strong foundation in exploratory data analysis (EDA) and statistics
• Experience with model evaluation and optimisation, including hyperparameter tuning and cross‑validation
• Hands‑on experience with cloud‑based ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
• Proficiency in SQL and working with large‑scale datasets
• Experience with version control and data science tools:
  • Git, Jupyter, Databricks
• Nice to have: Exposure to Computer Vision, Reinforcement Learning, or Graph ML
• Malaysian candidates preferred

Soft Skills & Professional Attributes

• Strong intellectual curiosity with an active technology‑watch mindset
• Ability to synthesize complex technical concepts into clear, actionable recommendations
• Critical and analytical approach to model outputs and AI‑generated code
• Pragmatic decision‑making with focus on ROI and business impact
• High level of scientific rigor in experimentation, validation, and documentation
• Excellent communication skills, both technical and business‑oriented
• Comfort working in ambiguous and evolving environments
• Proactive mindset with the ability to initiate ideas and make proposals
• Strong mentoring and pedagogical skills, supporting junior Data Scientists
• Agile, adaptive, and collaborative working style