AI Researcher

Key Responsibilities

Research & Data Analysis

• Analyse structured datasets to uncover patterns, trends, and insights that support research and business initiatives.
• Develop and apply predictive and statistical models such as regression, classification, and forecasting techniques.
• Conduct exploratory data analysis (EDA), feature engineering, and data quality assessments.
• Design and execute statistical experiments including hypothesis testing and model validation.
• Evaluate model performance and communicate findings, assumptions, and limitations clearly.

AI & Intelligent Systems Development

• Explore and implement LLM-powered agent frameworks for workflow automation and intelligent reasoning.
• Build AI agents capable of interacting with analytical tools, predictive models, and databases.
• Integrate AI systems with structured datasets and model pipelines to support end-to-end analytical tasks.
• Assess agent performance across reasoning quality, tool usage, reliability, and failure handling.
• Stay updated on emerging AI and agent technologies, sharing insights with the wider team.

Technical Development

• Develop clean, maintainable Python code for data processing, modelling, and visualisation tasks.
• Work with SQL databases for querying and managing structured data.
• Use standard data science and machine learning libraries such as Pandas, NumPy, Scikit-learn, and visualisation tools.
• Contribute to collaborative codebases and support reproducible experimentation and data workflows.

Collaboration & Communication

• Partner with researchers and technical stakeholders to deliver research initiatives and solution prototypes.
• Support preparation of technical reports and presentations for both technical and non-technical audiences.
• Participate in research discussions, peer reviews, and knowledge-sharing sessions.

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Mathematics, Statistics, Engineering, Physics, or related quantitative disciplines.
• Fresh graduates with strong academic projects, internships, research experience, competitions, or open-source contributions are welcome.

Technical Skills

• Strong Python programming skills with experience using data science and machine learning libraries.
• Understanding of data wrangling, feature engineering, and structured data processing.
• Good mathematical and statistical foundations relevant to machine learning.
• Familiarity with supervised, unsupervised, and generative modelling techniques.
• Basic knowledge of SQL and relational databases.

Personal Attributes

• Curious and research-oriented mindset.
• Strong analytical and problem-solving skills.
• Comfortable working in exploratory and evolving environments.
• Clear communicator with strong collaboration skills.
• Self-motivated and eager to learn new technologies and methodologies.