Data Scientist

7-9 Years

Job Description

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

Must Have Skills