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.