Intern, Synthetic Data for Predictive Analytics (Generative AI)

As a member of the advanced development team, the candidate will gain hands-on experience working in an R&D lab that leverages on AI (i.e., machine learning/deep learning) to address challenging real-world project use-cases. Specifically, the candidate will work on a project related to improving the AI model performance on small and imbalanced datasets using generative models. Overall, the candidate will support the team on:

• Exploratory data analysis (EDA), feature engineering, and optimize existing AI model’s performance.
• Experimentation and identification of suitable generative models for industrial datasets.
• Continued development of existing web-based machine learning (ML) application dashboard, analytical methods interface, and time-series database.
• Meticulously document development processes, algorithms, and performance metrics, contributing to the project's knowledge base.

Requirements

• Able to work full time for a period of at least 6 months.
• Undergraduate/Postgraduate currently pursuing a Degree/Masters in Computer Science, Computer Engineering, or a related technical discipline with an emphasis on machine and/or deep learning.
• Knowledge and experience dealing with time-series and imbalanced data (e.g., multivariate time-series forecasting, under-sampling, SMOTE etc.).
• Experience with deep learning methods, frameworks and visualization tools: Generative adversarial networks, tensorflow/pytorch, scikit-learn, dash, python, github.
• Curiosity-driven, independent thinking, and data-centric style of argument construction.
• Highly resourceful in gathering data from reliable and trustworthy sources, and the ability to summarize new knowledge fast.