Quantitative Researcher

BIPO SERVICE (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 50,000 - 80,000; Full time

Job summary

A quantitative investment firm in Singapore is seeking a Quantitative Researcher to develop and deploy systematic trading strategies. The role is ideal for recent graduates with strong quantitative training in mathematics, statistics, or computer science. Candidates should be proficient in Python and possess strong analytical skills to handle financial datasets. This position offers the opportunity to work closely with senior researchers and portfolio managers in a dynamic environment.

Qualifications

• Strong foundation in probability, statistics, and linear algebra.
• Experience with common scientific and data libraries.
• Ability to work with large datasets.

Responsibilities

• Research and develop predictive signals using various data sources.
• Build and evaluate predictive models using machine learning techniques.
• Work with engineering teams to deploy models into production.

Skills

Probability and statistics

Data analysis in Python

Problem-solving ability

Analytical skills

Education

Bachelor’s or Master’s degree in a quantitative discipline

Tools

Python

SQL

Job description

Ingold Capital is a CMS licensed fund management company. We focus on quantitative investment strategies, dedicated to producing exceptional returns for investors, by strictly adhering to mathematical and statistical methods, also through best‑in‑class risk management, compliance, and operations.

Job Descriptions

We are seeking a Quantitative Researcher to join our research team and contribute to the development, testing, and deployment of systematic trading strategies. This role is well suited for fresh graduates or early‑career professionals with strong quantitative training who are interested in applying mathematics, statistics, and machine learning to real‑world financial markets.

You will work closely with senior researchers, portfolio managers, and engineers to research alpha signals, build predictive models, and translate research insights into robust, production‑ready strategies.

Key Responsibilities

Quantitative Research & Alpha Development

• Research and develop predictive signals (alphas) using financial, alternative, and derived data.
• Apply statistical analysis, machine learning, and optimization techniques to identify and validate market inefficiencies.
• Conduct factor research, feature engineering, and signal combination across different time horizons.
• Perform rigorous backtesting, performance attribution, and statistical validation to ensure robustness and avoid overfitting.

Model Development & Evaluation

• Build and evaluate models using techniques such as Linear and non‑linear regression, Tree‑based methods, Neural networks / deep learning (where appropriate).
• Analyze model behavior across different market regimes and stress scenarios.
• Continuously improve existing models through iteration, refinement, and monitoring.

Collaboration & Production Support

• Work closely with engineering teams to translate research into production systems.
• Assist in model deployment, monitoring, and post‑launch performance analysis.
• Participate in research discussions, idea reviews, and internal knowledge sharing.

Documentation & Research Discipline

• Clearly document research hypotheses, methodologies, assumptions, and results.
• Maintain high standards of research reproducibility and transparency.

Job Requirements

Required Qualifications

Education

• Bachelor’s or Master’s degree in a quantitative discipline, such as:

1. Mathematics
2. Statistics
3. Computer Science
4. Physics
5. Engineering
6. Quantitative Finance or related fields

Technical Skills

1. Strong foundation in probability, statistics, and linear algebra.
2. Proficiency in Python for research and data analysis.
3. Experience with common scientific and data libraries.
4. Solid programming fundamentals.

Analytical & Research Skills

1. Strong problem‑solving ability and intellectual curiosity.
2. Ability to think rigorously about data, assumptions, and statistical significance.
3. Comfort working with large datasets and real‑world data.

Preferred / Nice‑to‑Have Qualifications

1. Prior exposure to financial markets, trading strategies, or asset pricing concepts.
2. Experience with machine learning or deep learning frameworks.
3. Experience with SQL, Linux, or high‑performance computing environments.
4. Internships or project experience in quantitative finance, data science, or related fields.