Data Scientist

Responsibilities

1. Design, develop, and deploy end-to-end machine learning, deep learning, and Generative AI solutions to solve complex business challenges and improve operational performance.
2. Perform advanced data analysis, feature engineering, statistical modelling, predictive analytics, and optimization techniques to generate actionable business insights.
3. Build, train, evaluate, and fine-tune machine learning and deep learning models, including transformer architectures, reinforcement learning models, neural networks, and large language models (LLMs).
4. Develop and implement GenAI applications using modern AI engineering frameworks such as LangChain, LangGraph, vector databases, embeddings, Retrieval-Augmented Generation (RAG), and enterprise-grade AI pipelines.
5. Design and manage scalable MLOps pipelines for model versioning, automated training, deployment, monitoring, governance, and lifecycle management on cloud platforms such as AWS.
6. Apply structured problem-solving methodologies, including MECE, Root Cause Analysis, hypothesis-driven analysis, and data-driven experimentation to address complex business and technical problems.
7. Collaborate closely with product managers, software engineers, cloud architects, business stakeholders, and domain experts to translate business requirements into AI and data science solutions.
8. Continuously evaluate emerging AI technologies, machine learning techniques, and Generative AI advancements to recommend innovative solutions and improve existing models.
9. Ensure data quality, model explainability, security, compliance, and governance while maintaining high standards of documentation, reproducibility, and best engineering practices.
10. Mentor junior data scientists and AI engineers, contribute to technical standards and knowledge sharing, and support the organization's AI capability development.

Requirements

1. Bachelor's, Master's, or PhD in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.
2. Strong proficiency in structured problem-solving frameworks such as MECE, Root Cause Analysis, hypothesis testing, and analytical thinking for solving complex business problems.
3. Proven experience in end-to-end machine learning model development, MLOps, CI/CD pipelines, model deployment, and cloud platforms (preferably AWS), with hands-on knowledge of production AI systems.
4. Deep technical expertise in machine learning, deep learning, Generative AI, transformer models, reinforcement learning, neural embeddings, LLMs, LangChain, LangGraph, vector databases, optimization techniques, statistical modelling, and multi-criteria decision analysis.
5. Strong programming skills in Python and data science libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch), with experience in SQL, cloud-native AI services, Git, and collaborative software development practices.

Clarence Khoh