AI Researcher (Statistical Modelling)

Role Overview

We are seeking a dedicated AI Researcher to become a vital member of our Research and Development team in Singapore. The role centers on advancing statistical and predictive models, harnessing machine learning and AI technologies to address concrete research challenges. A key part of the position involves utilizing large language model (LLM)-powered AI agents for intelligent data interaction.

Key Responsibilities

• Create and assess various predictive models such as regression, classification, time-series forecasting, and mathematical models.
• Conduct thorough statistical analyses including exploratory data analysis (EDA), hypothesis testing, power analysis, significance tests, and validation of models.
• Manage data processing tasks like data wrangling, feature engineering, and identification of outliers within structured datasets.
• Research and develop AI agents powered by LLMs that can reason, fetch data through queries, and utilize predictive or analytical models effectively.
• Construct reproducible machine learning workflows primarily using Python and evaluate the accuracy, performance, and limitations of developed models.
• Present research outcomes clearly to both technical teams and non-technical stakeholders.

Candidate Requirements

• Postgraduate qualifications (Master's or PhD) in quantitative disciplines such as Computer Science, Mathematics, Statistics, Physics, Engineering, or Data Science are highly preferred.
• Proven practical experience in applying predictive modelling techniques including regression, classification, and time-series forecasting.
• Solid grounding in statistical modelling methods and comprehensive experimental analysis skills.
• Advanced proficiency in Python programming along with libraries such as Pandas, NumPy, Scikit-learn, and SciPy; adept at data handling and feature engineering.
• Sound understanding of various machine learning approaches, encompassing supervised, unsupervised, and generative models.
• Experience or familiarity with large language models, AI agents, generative AI technologies, or autonomous agent frameworks is considered an asset.
• Strong analytical and research abilities, with competence in model evaluation and designing rigorous experiments.