Job Title: Python / Data Engineer – Quantitative Investment Support
Location: Onsite – Singapore
Employment Type: Contract
Experience Required: 4–8 years
About the Role
We are looking for a highly skilled Python/Data Engineer with strong experience in data manipulation, statistics, and machine learning models to support our Quantitative Investment team .
The ideal candidate should be comfortable working with dataframes, DuckDB (or similar technologies) , and have a good foundation in quantitative analysis.
This role involves building, maintaining, and optimizing data pipelines and providing analytical support for investment research.
Key Responsibilities
- Build, optimize, and maintain data pipelines and workflows for quantitative investment strategies.
- Perform data manipulation, cleaning, and transformation using Python (Pandas, Polars, DuckDB).
- Work closely with quantitative researchers and investment professionals to provide high-quality datasets and analytical tools.
- Support the development and validation of statistical and ML models for investment decision-making.
- Ensure data quality, accuracy, and reliability across multiple data sources.
- Collaborate with stakeholders to translate investment requirements into technical solutions .
- Automate data extraction, feature engineering, and reporting tasks to improve research efficiency.
- Contribute to performance monitoring and backtesting frameworks for quantitative models.
Required Skills & Qualifications
- Bachelor's/Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related field .
- 4–8 years of hands-on experience in Python programming with strong knowledge of dataframes (Pandas/Polars) and DuckDB/SQL .
- Strong understanding of statistics, probability, and ML models (regression, classification, time series).
- Experience working with financial datasets and supporting quantitative research is preferred.
- Familiarity with data visualization libraries (Matplotlib, Seaborn, Plotly).
- Good understanding of data structures, algorithms, and performance optimization .
- Strong problem-solving and analytical skills.
- Exposure to quantitative finance, portfolio optimization, or risk modeling .
- Experience with cloud platforms (AWS/Azure/GCP) for data engineering.
- Familiarity with big data technologies (Spark, Dask, PyArrow).
- Knowledge of backtesting frameworks and financial modeling tools .
- Experience in DevOps/CI-CD for data workflows .
- Ability to work in a fast-paced, research-driven environment .
- Strong communication skills to collaborate with quants, data scientists, and portfolio managers .
- Detail-oriented with a focus on data quality and reproducibility .
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