AI Researcher (2 Yr Contract)

KLASS ENGINEERING & SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

KLASS ENGINEERING & SOLUTIONS PTE. LTD. is seeking an AI researcher to design and build non-deterministic analytical tools based on statistical modelling, predictive models, and machine learning across structured and unstructured data.

The role includes developing AI agents powered by large language models that can reason over and orchestrate these tools to solve analytical problems end-to-end. The researcher will apply rigorous data practices, explore intelligent systems, and pursue solutions

Qualifications

• Master’s or PhD in Computer Science, Mathematics, Statistics, Physics, Engineering, Data Science, or closely related field.
• Strong Python programming with data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn).
• Experience with data wrangling, missing data handling, feature engineering, and normalization on structured datasets.
• Foundations in linear algebra, calculus, and optimization applied to ML and statistics.

Responsibilities

• Conduct research to identify patterns, trends, and statistically significant findings to inform decisions.
• Develop predictive models and mathematical approaches for defined problems.
• Design and run experiments, validate model performance, and report limitations.
• Collaborate to study LLM-based agents, automate research workflows, and present findings.

Skills

Python

Data Wrangling

ML & Modelling

Statistical Methods

Education

Master’s or PhD in a quantitative field

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)

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.