Research Assistant(Data Science and Analytics)

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

Research Assistant(Data Science and Analytics)

• Design, build and maintain end‑to‑end AI data pipelines for construction‑site AI projects, including multi‑modal (RGB + IR) dataset acquisition, pre‑processing, alignment, quality control via clustering methods (KMeans, HDBSCAN), data augmentation and annotation standardisation; improve dataset consistency for facade defect detection, on‑site hazard detection and intelligent tower crane modules.
• Develop, train, tune and benchmark deep‑learning models using PyTorch for construction computer‑vision tasks: object detection (YOLO family), keypoint detection and pose estimation for tower‑crane hook stabilisation and site safety monitoring. Measure recall / precision / accuracy and iterate augmentation strategies and model configurations to lift real‑world performance metrics.
• Support data engineering for the AI‑powered Intelligent Tower Crane System: manage on‑site raw data collection, cleaning and validation for hazard detection and hook‑stabilisation modules; analyse system performance and contribute to accuracy and operational efficiency improvements.
• Implement Retrieval‑Augmented Generation (RAG) pipelines for internal research; integrate multi‑retriever modules (BM25, dense retrievers, ColBERT, E5 / GTE‑Qwen‑Instruct), open‑source LLMs, conduct quantitative evaluation using MAP, NDCG, precision and recall metrics. Explore potential LLM / agent‑AI use cases for construction‑site documentation, incident analysis and technical knowledge retrieval.
• Build NLP solutions such as text tokenisation, feature extraction and supervised text classification; process structured / unstructured text data including project reports, site incident records and technical documentation.
• Perform statistical analysis and exploratory data analysis (EDA) using Python and R; conduct quantitative analysis on construction‑site operational data, safety incident datasets and experimental model outputs.
• Construct and optimise ETL workflows; manage SQL and NoSQL (MongoDB) databases, integrate multi‑source datasets to build AI‑ready data infrastructure for construction‑tech research and decision‑making.
• Collaborate cross‑functionally with research and engineering teams, analyse model error cases from real‑world site data, document experimental workflows, model performance and technical findings; support report generation and data visualisation via Power BI / Matplotlib / Seaborn.
• Keep track of state‑of‑the‑art advances in LLM, agentic AI systems, multimodal deep learning and computer vision; prototype new techniques and adapt them for construction robotics, site safety and intelligent tower‑crane research projects.

RequirementsEducation

• 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.
• Familiar with statistical methods, exploratory data analysis; hands‑on experience evaluating CV / NLP models with metrics including MAP, NDCG, recall, precision, AUC.
• Working knowledge of visualisation tools: Power BI, Pandas, NumPy, Matplotlib, Seaborn.