Morgan McKinley Hiring! Full Time Senior Data Scientist in Federal Territory - Ricebowl

Title : Senior Data Scientist – Technology Watch & Model Development

Location: Putrajaya

Employment type: Permanent:

Employment mode: Onsite

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.

Mission Description

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.

The assignment takes place in an English-speaking environment; fluency in English is mandatory.

Requested skills

Technical skills Skills :

• At least 7 years of experience in Data Science / Machine Learning
• Proven experience in the banking/finance sector (understanding of business challenges)
• Demonstrated experience in vibe coding with productive use of AI tools for rapid prototyping
• Expertise in ML/DL: supervised learning, unsupervised learning, deep learning, NLP, time series
• Proficiency in Python frameworks: scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM
• In-depth knowledge of LLMs and modern techniques (fine-tuning, RAG, prompt engineering, agents)
• Expertise in feature engineering and variable selection
• Strong skills in exploratory data analysis and statistics
• Experience in model evaluation and optimisation (hyperparameter tuning, cross-validation)
• Knowledge of Cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
• Proficiency in SQL and handling large-scale datasets
• Knowledge of version control tools (Git) and notebooks (Jupyter, Databricks)
• Nice to have: Experience in Computer Vision, Reinforcement Learning or Graph ML

Soft Skills :

• intellectual curiosity and active technology watch
• Strong synthesis and recommendation skills (translating technical complexity into actionable insights)
• Critical mindset towards model results and AI-generated codePragmatism: ability to quickly assess the ROI of a technical approach
• Scientific rigour in experimentation and validation
• Excellent communication, both technical and business-oriented
• Ability to work in uncertainty and ambiguity
• Initiative and ability to make proposals
• Pedagogy and mentoring (support to the junior Data Scientist)
• Agile and adaptive mindset

Expected deliverables

Technology watch and feasibility:

Weekly technology watch on AI advances (papers, new approaches, tools)

• Rapid technical feasibility assessments of business use cases (2–3 days max)
• Benchmark of market solutions (vendors vs open-source) with decision matrix
• Quick POCs to validate hypotheses before major investment
• Clear and actionable technical recommendations for decision-makers
• Participation in business workshops to understand needs

AI model development:

Design and development of ML/DL models for priority use cases

• Justified choice of algorithms and technical approaches (baseline, state-of-the-art)
• Feature engineering and model optimisation (performance, robustness)
• Rapid prototyping using vibe coding (POC in 2–4 weeks)
• Rigorous validation of models (metrics, robustness, bias)
• Comprehensive technical documentation (notebooks, methodology, results)
• Collaboration with AI developers for industrialisation

Mentoring and knowledge sharing:

Support to the junior Data Scientist (pair programming, code reviews)

• Knowledge transfer on advanced techniques and best practices
• Sharing of technology watch findings with the team (tech talks, documentation)
• Contribution to the library of prompts and vibe coding techniques
• Facilitation of technical workshops and feedback sessions