Analyst

MILLENNIUM CAPITAL MANAGEMENT (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A leading financial services firm in Singapore is looking for an Analyst to conduct research and analyze alternative datasets. The ideal candidate has a Master's degree in Financial Engineering and a year of experience in quantitative analysis, proficient in Python and statistical methods. Responsibilities include building ETL processes, creating monitoring systems, and utilizing machine learning tools to provide insights to Portfolio Managers.

Qualifications

• Minimum 1 year in quantitative research and analysis within finance.
• Familiarity with Bayesian inferencing and PCA.
• Experience with Python libraries such as pandas and sklearn.

Responsibilities

• Conduct research on proprietary and alternative datasets.
• Build ETL processes to clean and tag datasets.
• Create monitoring systems to detect data outliers.

Skills

Statistical techniques

Python

Machine learning algorithms

Database skills

Education

Master's degree in Financial Engineering or related field

Tools

Linux

AWS

Docker

Job description

Key Responsibilities

• The Analyst will conduct research on proprietary and alternative datasets
• Build Extract, Transform, and Load (ETL) processes to ingest, clean, and tag datasets
• Create monitoring system to detect data outliers, collection issues, and other anomalies
• Analyze datasets and create statistical models to extract fundamental insights for Portfolio Managers and Analysts
• Tackle challenging quant problems using cutting-edge machine learning and deep learning toolkits
• Write scalable, production code for model deployment
• Maintain ownership of datasets and act as domain expert

Qualifications/Skills Required

• Minimum Requirements: Requires a Master's degree in Financial Engineering, or a related quantitative field, plus 1 year in a professional quantitative research and analysis experience in the financial or investment industry
• Must include 1 year with each of the following:

1. Statistical techniques, including Bayesian inferencing, Principal Component Analysis (PCA), and random forests
2. Analysis and visualization of alternative datasets
3. Python, including pandas, sklearn, tensorflow, pytorch, keras, and statsmodels
4. Linux, AWS, and Docker for cloud-based machine learning workflows
5. Convex optimization, linear algebra, and probability theory
6. Machine learning algorithms, including decision trees, neural networks, genetic programming, boosting algorithms, and ensemble methods
7. Implementing machine learning algorithms to predict returns, including data cleaning, feature engineering, feature selection, cross-validation, hyperparameter tuning, and model deployment
8. Equity statistical arbitrage
9. Object-oriented programming (OOP) and data structures, including hash tables, trees, heaps, and graphs
10. Algorithms including DFS, BFS, dynamic programming, and topology sorting
11. Market microstructure, including order flow, trade imbalance, and market impact
12. Database skills, including SQL and KDB