What you'll be doing
As part of IT Innovation projects, you will join the AI Factory/Innovation team as a Senior Data Scientist with a dual strategic role: technology watch and AI model development.
Mission 1 – Technology watch and feasibility: You will be the technical reference for new AI/ML approaches (LLMs, new architectures, emerging techniques). You will assess the technical feasibility of business use cases, benchmark market solutions (vendors, open-source), and deliver quick POCs (2–3 days) to validate hypotheses before major investment.
Mission 2 – AI model development: You will design and develop ML/DL models for selected use cases, from algorithm selection to final optimisation. You will build rapid prototypes (POCs in 2–4 weeks) and support a junior Data Scientist in developing their skills.
As a vibe coding expert, you use generative AI tools (GitHub Copilot, Cursor, Claude, ChatGPT) to accelerate data exploration, model prototyping, analysis code generation and documentation, while maintaining a critical mindset regarding the results.
You will work in an agile mode, closely with the Products & Innovation business teams, AI developers, architects and other Data Scientists.
Targeted profile : Senior Data Scientist with banking/finance experience, expert in ML/DL and proficient in vibe coding, able to quickly assess the technical feasibility of AI use cases, develop POCs, and stay at the forefront of technological advances.
Key Responsibilities
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
Skill Requirements
- 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