Senior Quantitative Researcher - AI-Driven Markets

meridian & saturn capital pte. ltd.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

MS Capital, a private fund management company, is expanding its team and seeks experienced candidates with strong programming, statistics, and data analysis backgrounds. The role focuses on developing strategies for international and secondary markets using quantitative methods.

You will apply Python and/or C++ to build research pipelines, backtesting systems, or live trading infrastructure, and work to extract patterns from diverse data to fuel robust models and continuous improvement.

Qualifications

• Bachelor's degree or above in finance, math/stats, physics, CS, engineering, or related field.
• 3+ years of experience in quantitative research, systematic trading, or related domains.
• Strong proficiency in Python and/or C++ with production-level coding experience.
• Proven track record of developing, validating, and deploying quantitative models in live trading.
• Deep understanding of statistical methods, time-series analysis, and financial data structures.

Responsibilities

• Research and develop international market trading strategies.
• Leverage programming and analytical tools to drive quantitative strategies through a data-driven approach.
• Extracting patterns from market microstructure, trading, fundamentals, events and other multivariate data to build diversified quantitative strategy models.

Skills

Python

C++

Quantitative research

Time-series analysis

Data pipelines

Education

Bachelor's degree or higher

Job description

MS Capital, a private fund management company, is expanding its team and seeks experienced candidates with strong programming, statistics, and data analysis backgrounds. The role focuses on developing strategies for international and secondary markets using quantitative methods.

You will apply Python and/or C++ to build research pipelines, backtesting systems, or live trading infrastructure, and work to extract patterns from diverse data to fuel robust models and continuous improvement.