AI Data Science Research Assistant — Multimodal CV & LLMs

CraneR Technology Limited; Hong Kong; On-site; HKD 300,000 - 480,000; Full time

Job summary

CraneR Technology Limited is seeking a Research Assistant (Data Science and Analytics) to design, build and maintain end-to-end AI data pipelines for construction-site AI projects, including multimodal data acquisition, preprocessing and annotation standardisation. You will develop and benchmark PyTorch models for computer vision and NLP tasks using Python, R and SQL.

Beyond model development, you will support data engineering, analyze system performance, implement RAG pipelines, and contribute

Qualifications

• Master’s degree in Data Science & Analytics, Statistics, Computer Science, or related quantitative disciplines; Bachelor’s degree in Statistics or relevant STEM field is required.
• Exposure to multimodal learning, computer vision or applied data analytics for engineering domains is an advantage.
• Strong programming skills in Python, PyTorch; practical experience with R, SQL, Git.
• Hands-on experience evaluating CV / NLP models with metrics including MAP, NDCG, recall, precision, AUC.

Responsibilities

• Design, build and maintain end-to-end AI data pipelines for construction-site AI projects; multimodal dataset acquisition and annotation standardisation.
• Develop, train, tune and benchmark deep-learning models using PyTorch for CV and NLP tasks; measure performance and iterate improvements.
• Support data engineering for AI-powered systems; manage on-site data collection, cleaning, validation and analysis.

Skills

Python

PyTorch

Data analysis

NLP

CV

Education

Master’s degree in Data Science & Analytics

Bachelor’s degree in Statistics or related STEM field

Tools

Python

PyTorch

R

SQL

Git

MongoDB

Power BI

Pandas

NumPy

Matplotlib

Seaborn

Job description

CraneR Technology Limited is seeking a Research Assistant (Data Science and Analytics) to design, build and maintain end-to-end AI data pipelines for construction-site AI projects, including multimodal data acquisition, preprocessing and annotation standardisation. You will develop and benchmark PyTorch models for computer vision and NLP tasks using Python, R and SQL.

Beyond model development, you will support data engineering, analyze system performance, implement RAG pipelines, and contribute