Quantitative Researcher & Data Analyst

Role Title: Quantitative Researcher & Data Analyst

Position Overview:

This role focuses on asset allocation research, quantitative modeling, database engineering, and data visualization to support systematic strategies, factor research, and custom portfolio construction for key internal and external stakeholders.

Key Responsibilities:

- Research & Portfolio Analysis: Conduct asset allocation and manager evaluation research; perform custom portfolio analysis and construct tailored portfolios for key stakeholders.

- Data Cleansing & Database Architecture: Design and build new RDBMS databases using multi-source data; set up infrastructure, data pipelines, collation, and cleansing protocols (SQL, Python).

- Quantitative Modeling & Analytics: Apply statistical modeling, machine learning, factor research, and systematic modeling (Python) to solve complex quantitative problems.

- Data Visualization & Reporting: Build interactive dashboards using Python Dash; clean and manipulate large datasets to generate automated research reports.

- AI & Workflow Automation: Leverage modern AI tools to accelerate data handling, quantitative research, and the modeling lifecycle.

Technical & Academic Requirements:

- Database & SQL (Mandatory): 2+ years of RDBMS database design, specifically with MS SQL Server; proven capability manipulating large-scale datasets.

- Python Engineering (Mandatory): 2+ years of hands-on Python development; advanced mastery of pandas, numpy, and statsmodels (familiarity with dash, pypfopt, cvxpy, keras, scikit-learn).

- Core Competencies (Mandatory): High attention to detail and accuracy; fluent English communication for technical and non-technical stakeholder interactions.

- Quantitative Modeling (Preferred): Working experience building quantitative models, factor research, portfolio construction, and systematic strategies.

- Education (Preferred): Degree in Mathematics, Physics, Statistics, Econometrics, Computer Science, Engineering, or a related quantitative field.

- Domain & AI Exposure (Bonus): Foundational understanding of financial statements, accounting, risk analysis, and practical experience leveraging AI tools in quantitative workflows.