AI Researcher (Contract)

QUESS SELECTION & SERVICES PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

QUESS SELECTION & SERVICES PTE. LTD. is seeking a seasoned quantitative model developer to research, design, train, and validate deep learning models for real-world AI applications. You will work with high-frequency data and deploy production-ready models in a dynamic environment.

Responsibilities include experimenting to validate performance, refining algorithms, and monitoring advances in data science and computation to ensure robust, efficient solutions.

Qualifications

• Master’s degree or above in a related field.
• Strong programming skills, especially in Python.
• 3+ years of experience in at least one programming language: Python, C++, C, Java, or Rust, with strong skills in code organisation and performance optimisation.
• Solid understanding of time series analysis, machine learning, signal processing, and related theories, with experience independently developing models.
• Strong foundation in probability theory, mathematical statistics, and optimization theory.
• Experience in developing, validating, and deploying quantitative models in production environments is preferred.
• Ability to explain technical model decisions clearly, including architecture choice, loss function selection, and performance trade-offs.
• Practical mindset with the ability to move applied research into deployment-ready solutions.

Responsibilities

• Research and develop quantitative models to identify patterns and short-term trends in large-scale datasets.
• Design, train, fine-tune, evaluate, and improve deep learning models for real-world AI applications.
• Conduct experiments to validate model performance, reliability, and generalisation on unseen data.
• Perform feature extraction, signal analysis, factor analysis, and modeling using high-frequency data.
• Build or optimize simulation and model evaluation frameworks to assess accuracy, robustness, and computational efficiency.
• Continuously refine model parameters and algorithms to improve performance, processing efficiency, and stability.
• Monitor and evaluate the latest developments in data science, academic research, and advanced computational technologies.

Skills

Python

C++

C

Java

Rust

Time series analysis

Machine learning

Signal processing

Probability theory

Optimization theory

Model deployment

Explainable AI

Education

Master’s degree or above in Mathematics/Statistics/Physics/CS/Electronic Engineering

Job description

Responsibilities

• Research and develop quantitative models to identify patterns and short-term trends in large-scale datasets.
• Design, train, fine-tune, evaluate, and improve deep learning models for real-world AI applications
• Conduct experiments to validate model performance, reliability, and generalisation on unseen data.
• Perform feature extraction, signal analysis, factor analysis, and modeling using high-frequency data.
• Build or optimize simulation and model evaluation frameworks to assess accuracy, robustness, and computational efficiency.
• Continuously refine model parameters and algorithms to improve performance, processing efficiency, and stability.
• Monitor and evaluate the latest developments in data science, academic research, and advanced computational technologies.

Requirements

• A master’s degree or above, preferably in Mathematics, Statistics, Physics, Computer Science, Electronic Engineering, or a related field.
• Strong programming skills, especially in Python.
• 3+ years of experience in at least one programming language: Python, C++, C, Java, or Rust, with strong skills in code organisation and performance optimisation.
• A solid understanding of time series analysis, machine learning, signal processing, and related theories, with experience independently developing models.
• A strong foundation in probability theory, mathematical statistics, and optimization theory.
• Experience in developing, validating, and deploying quantitative models in production environments is preferred.
• Ability to explain technical model decisions clearly, including architecture choice, loss function selection, and performance trade-offs.
• Practical mindset with the ability to move applied research into deployment-ready solutions.