AI Researcher (2 Yr Contract)

KLASS ENGINEERING & SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

KLASS Engineering & Solutions PTE. LTD. is seeking an AI researcher to design and build non-deterministic analytical tools, grounded in statistics, predictive models, and ML, across structured and unstructured data.

The researcher will also develop AI agents powered by large language models to reason over data and orchestrate tools to solve analytical problems end to end. You will be rigorous with data, curious about intelligent systems, and motivated by seeing your work run in production.

Qualifications

• Master’s or PhD in Computer Science, Mathematics, Statistics, Physics, Engineering, Data Science, or closely related quantitative discipline.
• Bachelor’s Degree in a relevant quantitative field, with hands-on data science experience (internships, projects, competitions).
• Fresh Graduates – encouraged to apply; strong fundamentals and intellectual curiosity are valued.

Responsibilities

• Work with structured datasets to identify patterns and statistically significant findings informing research questions.
• Apply predictive models (regression, classification, time series) to address defined problems.
• Conduct exploratory data analysis to assess data quality, distributions, and feature relevance.
• Design and run statistical experiments including hypothesis testing and power analysis.
• Collaborate with AI development to integrate data science models into agent workflows.

Skills

Python

Data wrangling

Mathematical foundations

Modelling knowledge

Education

Master’s or PhD in a quantitative field

Bachelor’s degree in a relevant quantitative field

Fresh graduates encouraged

Tools

Pandas

NumPy

Scikit-learn

SciPy

Matplotlib

Seaborn

Job description

Summary

This is an AI researcher role. The researcher will design and build non-deterministic analytical tools — grounded in statistical modelling, predictive models, and machine learning — that work across structured and unstructured data. Another part of the role is developing AI agents powered by large language models (LLMs) that can reason over and orchestrate these tools to solve real analytical problems end to end. The researcher is expected to be rigorous with data, curious about intelligent systems, and motivated by seeing their work produce something that actually runs.

Responsibilities

Research & Analysis

• Work with structured datasets to identify patterns, trends, and statistically significantfindings that inform research questions and business decisions.
• Apply and adapt predictive models (regression, classification, time series forecasting) andmathematical models to address defined research problems.
• Conduct exploratory data analysis (EDA) to assess data quality, distributions, and featurerelevance prior to modelling.
• Design and run statistical experiments, including hypothesis testing, power analysis, and significance testing.
• Validate model performance using appropriate evaluation frameworks and communicate limitations honestly.

AI development

• Study and apply LLM-based agent frameworks to automate and orchestrate researchworkflows.
• Design and build AI agents that use LLMs as a reasoning engine, with data sciencemodels and pipelines exposed as callable tools.
• Integrate agent outputs with structured data — enabling agents to query databases,invoke predictive models, and interpret results.
• Evaluate agent performance: task completion, reasoning coherence, tool-use accuracy,hallucination rate, and failure handling.
• Stay current with the evolving agent landscape and share learnings with the team.

Technical development

• Write clean, well-documented Python code for data processing, feature engineering,model development, and result visualisation.
• Query and manipulate structured data from relational databases using SQL.
• Use core data science libraries including Pandas, NumPy, Scikit-learn,Matplotlib/Seaborn, etc.
• Contribute to shared codebases, following team coding standards and version controlpractices (KLASS Gitlab).
• Assist in building reproducible data pipelines and experiment tracking workflows.

Collaboration & communication

• Work closely with senior researchers and principal researchers to scope, plan, and deliverproject workstreams.
• Contribute to the quarterly and annual research reports, translating technical findings intoclear, accessible language.
• Present findings to internal and client-side collaborators, including non-technicalaudiences.
• Participate in team research reviews, peer code reviews, and knowledge-sharingsessions.

Requirements

Education

• Master’s or PhD –Computer Science, Mathematics, Statistics, Physics, Engineering, Data Science, or a closely related quantitative discipline. (Preferred)
• Bachelor’s Degree – In a relevant quantitative field, with demonstrable hands-on experience in data science (e.g. internships, research projects, competitions, or open-source contributions).
• Fresh Graduates – Encouraged to apply. We value strong fundamentals and intellectual curiosity over years of experience.

Technical skills — essential

• Python programming: proficiency with core data science libraries (Pandas, NumPy,Scikit-learn, SciPy, Matplotlib, Seaborn).
• Data wrangling: handling missing data, outlier detection, feature engineering, andnormalisation on structured datasets.
• Mathematical foundations: linear algebra, calculus, and optimisation as applied tomachine learning and statistical modelling.
• Working knowledge of modelling families: supervised, unsupervised, and generativemodels

Personal Qualities

1. Research mindsetIntellectually curious across both quantitative and AI domainsRigorous and detail-oriented in analysis and documentationComfortable with ambiguity and iterative problem-solvingHonest about uncertainty — in models and in LLM outputs
2. Working styleClear communicator, written and verbalCollaborative team player in a research environmentSelf-directed with good time managementEager to learn and receive constructive feedback

Experience

0–2 years of professional or research experience

We define experience broadly. Relevant experience includes academic research projects, dissertations with a data science component, internships, industry placements, personal or open-source projects, Kaggle competitions, or research assistant roles. We are more interested in the quality of your analytical thinking than the number of years on your CV.