Senior Quantitative Researcher

IMPECCABLE TECH PTE. LTD.; Singapore; On-site; SGD 180,000 - 320,000; Full time

Job summary

IMPECCABLE TECH PTE. LTD. in Singapore invites a Senior Quantitative Researcher to lead research and develop advanced quantitative models, ML methods, and data-driven solutions for complex tech and business problems.

The role requires a strong academic foundation in quantitative disciplines and hands-on experience in ML, time-series analysis, and large-scale data processing. Collaboration with engineering teams to deploy scalable, production-grade solutions is essential.

Qualifications

• Master’s degree or PhD in Computer Science, Mathematics, Statistics, Operations Research, Engineering, Data Science or another highly quantitative discipline.
• Minimum 5 years of relevant professional experience in quantitative research, machine learning, data science, statistical modelling or advanced analytics.
• Demonstrated experience owning end-to-end quantitative research, including hypothesis formulation, feature engineering, model development, validation, testing and implementation.
• Advanced proficiency in Python, with practical experience in at least one additional language such as C/C++, R, Java or Scala.
• Strong knowledge of statistical modelling, machine learning, time-series analysis, optimisation, clustering and pattern recognition.
• Strong practical understanding of both supervised and unsupervised machine learning, including model selection, validation and performance evaluation methodologies.
• Experience developing quantitative models involving time-series or sequential data, including forecasting or anomaly detection.
• Extensive hands-on experience in feature engineering and analyzing large-scale datasets.
• Strong knowledge of SQL and database technologies; familiarity with Spark/Hive/Presto/Redshift is advantageous.
• Experience translating research prototypes into production-ready implementations.

Responsibilities

• Lead end-to-end quantitative research initiatives from problem formulation to implementation.
• Design and develop advanced statistical, machine learning and quantitative models for complex problems.
• Analyze large-scale, noisy, high-dimensional datasets to identify predictive patterns and anomalies.
• Develop feature engineering and signal discovery methodologies for meaningful predictive features.
• Apply supervised/unsupervised learning, time-series analysis, clustering and optimization to quantitative problems.
• Design and implement robust evaluation frameworks to test hypotheses and assess model robustness.
• Develop research infrastructure, reusable tools and data pipelines to support scalable analysis.
• Collaborate with software and data engineers to translate research into production-grade solutions.
• Communicate complex findings to senior stakeholders across locations.
• Manage multiple projects with high-quality outputs within tight timelines.

Skills

Python

Machine Learning

Time-series analysis

Statistical modelling

SQL

Data processing

Feature engineering

C/C++

Education

Master’s degree or PhD in a quantitative discipline

Tools

Spark

Hive

Presto

Redshift

Job description

Role Overview

We are seeking an experienced Senior Quantitative Researcher to lead the research and development of advanced quantitative models, machine learning methodologies and data-driven solutions for complex technology and business applications.

The successful candidate will have a strong academic foundation in computer science, mathematics, operations research or a related quantitative discipline, combined with hands‑on experience in machine learning, statistical modelling, time‑series analysis and large‑scale data processing.

This is a highly technical role requiring the ability to independently formulate research problems, develop and validate sophisticated quantitative models, and work closely with engineering and data teams to translate research outcomes into scalable, production‑ready technology solutions.

Key Responsibilities

• Lead end-to-end quantitative research initiatives, from problem formulation and hypothesis development through feature engineering, model development, validation, testing and implementation.
• Design and develop advanced statistical, machine learning and quantitative models to address complex analytical and technology problems.
• Analyse large-scale, high‑volume, noisy and high‑dimensional datasets from multiple structured and unstructured sources to identify predictive patterns, relationships and anomalies.
• Develop sophisticated feature engineering and signal discovery methodologies, transforming complex raw datasets into meaningful predictive features and quantitative indicators.
• Apply supervised and unsupervised machine learning, statistical modelling, time‑series analysis, clustering, optimisation and pattern recognition techniques to complex quantitative problems.
• Design and implement robust simulation, backtesting and systematic model evaluation frameworks to test quantitative hypotheses and evaluate model accuracy, stability and robustness.
• Conduct detailed model performance analysis, including identifying overfitting, performance degradation, changing data patterns and other factors affecting model reliability.
• Design and enhance quantitative research infrastructure, reusable analytical frameworks and research tools to improve experimentation efficiency, scalability and reproducibility.
• Develop data processing methodologies and work with engineering teams on data pipelines required to support large‑scale quantitative analysis.
• Independently formulate research hypotheses, design appropriate experiments and determine quantitative methodologies based on empirical evidence.
• Identify opportunities to apply machine learning, artificial intelligence and advanced quantitative techniques to improve existing technology systems, processes and analytical capabilities.
• Work closely with software engineers and data engineers to translate research models and prototypes into scalable, production‑ready technology solutions.
• Provide technical direction on quantitative methodologies, model selection, feature design, validation approaches and research best practices.
• Review quantitative research methodologies and technical outputs and contribute to the continuous development of the organisation’s quantitative research capabilities.
• Communicate complex research findings, methodologies and technical recommendations clearly to senior stakeholders and technical teams across different geographical locations.
• Independently manage multiple complex research initiatives and deliver high‑quality technical solutions within demanding timelines.

Requirements

• Master’s degree or PhD in Computer Science, Mathematics, Statistics, Operations Research, Engineering, Data Science or another highly quantitative discipline.
• Minimum 5 years of relevant professional experience in quantitative research, machine learning, data science, statistical modelling or advanced analytics.
• Demonstrated experience owning end‑to‑end quantitative research, including hypothesis formulation, feature engineering, model development, validation, testing and implementation.
• Advanced proficiency in Python, together with practical experience in at least one additional programming language such as C/C++, R, Java or Scala.
• Strong knowledge of statistical modelling, machine learning, time‑series analysis, optimisation, clustering and pattern recognition.
• Strong practical understanding of both supervised and unsupervised machine learning, including appropriate model selection, validation and performance evaluation methodologies.
• Demonstrated experience developing quantitative models involving time‑series or sequential data, including forecasting, predictive modelling or anomaly detection.
• Extensive hands‑on experience in feature engineering, including deriving and evaluating predictive features from large‑scale and complex raw datasets.
• Extensive experience processing and analysing large‑scale, noisy, high‑dimensional and computationally intensive datasets from multiple data sources.
• Hands‑on experience designing or developing simulation, backtesting, experimentation or systematic model evaluation frameworks for quantitative research.
• Strong understanding of model robustness and validation, including overfitting, model degradation and performance changes across different data environments.
• Experience developing quantitative research infrastructure, analytical frameworks, reusable research tools or libraries to improve research efficiency and reproducibility.
• Strong knowledge of SQL and database technologies. Experience with large‑scale or distributed data processing technologies such as Spark, Hive, Presto, Redshift or equivalent would be advantageous.
• Experience working closely with software and data engineering teams to translate quantitative research prototypes into production‑ready implementations.
• Strong programming and algorithmic problem‑solving capabilities, with the ability to independently develop research tools and analytical solutions where required.
• Demonstrated ability to independently formulate complex quantitative research problems, select appropriate methodologies, design experiments and evaluate results based on empirical evidence.
• Professional proficiency in both Chinese and English is required to conduct technical discussions and communicate complex quantitative concepts effectively with stakeholders and technical teams operating in both languages.
• Strong written and verbal communication skills, including the ability to explain sophisticated quantitative methodologies to both technical and non‑technical stakeholders.
• Ability to operate independently, manage multiple complex research initiatives and deliver high‑quality results in a fast‑paced technology environment.

Preferred Qualifications

• Advanced research experience in computer science, mathematics, operations research, machine learning or another highly quantitative field.
• Experience combining statistical, machine learning and time‑series methodologies to solve complex quantitative problems.
• Experience working with both high‑volume/high‑frequency datasets and longer‑horizon time‑series datasets.
• Experience with C/C++ in addition to Python, particularly for computationally intensive applications.
• Experience designing internal research infrastructure, analytical frameworks or reusable quantitative libraries.
• Experience applying quantitative methodologies across multiple datasets, domains or geographical markets.
• Experience operating in multidisciplinary environments involving quantitative researchers, software engineers and data engineers.