- Kuala Lumpur Federal Territory Malaysia
全职 Senior Technical Lead 工作, 薪水, HCLTech Federal Territory 公司招聘中 - Ricebowl
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职位描述
岗位职责
Kuala Lumpur, Federal Territory of Kuala Lumpur
Job Summary
About the role
HCL Technologies Malaysia SDN BHD is seeking a motivated and talented Senior Data Scientist to join our team in Kuala Lumpur. This full-time position offers the opportunity to work on cutting-edge projects and collaborate with a dynamic team of professionals.
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 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
What we're looking for
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
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 code Pragmatism: 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
Skill Requirements
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
What we're looking for
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
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 code Pragmatism: 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
Other Requirements
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
What we're looking for
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
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 code Pragmatism: 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
************* ************* .unify-apply-now: focus, ************* ************* ************* ************* .unify-apply-now: focus, ************* ************* .unify-apply-
Job Summary
About the role
HCL Technologies Malaysia SDN BHD is seeking a motivated and talented Senior Data Scientist to join our team in Kuala Lumpur. This full-time position offers the opportunity to work on cutting-edge projects and collaborate with a dynamic team of professionals.
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 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
What we're looking for
- Bachelor's or Master degree in information technology or equivalent
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
- Malaysian Candidates Preferred
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 code Pragmatism: 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
Skill Requirements
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
What we're looking for
- Bachelor's or Master degree in information technology or equivalent
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
- Malaysian Candidates Preferred
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 code Pragmatism: 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
Other Requirements
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
What we're looking for
- Bachelor's or Master degree in information technology or equivalent
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
- Malaysian Candidates Preferred
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 code Pragmatism: 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
************* ************* .unify-apply-now: focus, ************* ************* ************* ************* .unify-apply-now: focus, ************* ************* .unify-apply-
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