Data Scientist

Responsibilities

• Design, develop, and deploy end-to-end machine learning, deep learning, and Generative AI solutions to solve complex business challenges and improve operational performance.

• Perform advanced data analysis, feature engineering, statistical modelling, predictive analytics, and optimization techniques to generate actionable business insights.

• 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).

• 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.

• Design and manage scalable MLOps pipelines for model versioning, automated training, deployment, monitoring, governance, and lifecycle management on cloud platforms such as AWS.

• Apply structured problem-solving methodologies, including MECE, Root Cause Analysis, hypothesis-driven analysis, and data-driven experimentation to address complex business and technical problems.

• Collaborate closely with product managers, software engineers, cloud architects, business stakeholders, and domain experts to translate business requirements into AI and data science solutions.

• Continuously evaluate emerging AI technologies, machine learning techniques, and Generative AI advancements to recommend innovative solutions and improve existing models.

• Ensure data quality, model explainability, security, compliance, and governance while maintaining high standards of documentation, reproducibility, and best engineering practices.

• Mentor junior data scientists and AI engineers, contribute to technical standards and knowledge sharing, and support the organization's AI capability development.

Requirements

• Bachelor's, Master's, or PhD in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related quantitative discipline.

• Strong proficiency in structured problem-solving frameworks such as MECE, Root Cause Analysis, hypothesis testing, and analytical thinking for solving complex business problems.

• 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.

• 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.

• 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