Artificial Intelligence & Machine Learning, Off

Job Description – Quantitative Analysis Officer

Team: SSIM Data, Analytics and AI Services (DAAIS)
Location: Hangzhou

Role Overview

The Quantitative Analysis Officer will be a core member of the SSIM DAAIS team, responsible for delivering rigorous quantitative analysis and data-driven insights to support investment, distribution, marketing, and strategic decision-making across SSIM.
The role combines statistical analysis, data modeling, and business interpretation, with close collaboration across technology, business, and investment stakeholders.

Key Responsibilities

• Perform quantitative analysis on large, multi-source datasets to support SSIM business and investment initiatives
• Design, develop, and maintain analytical models (e.g., statistical models, predictive models, attribution or forecasting frameworks) aligned with business objectives
• Conduct data exploration, feature engineering, and validation to ensure analytical robustness and interpretability
• Translate complex analytical results into clear, actionable insights for non-technical stakeholders
• Partner with business teams to understand decision contexts and define analytical problem statements
• Support model documentation, governance, and ongoing monitoring in line with SSIM standards
• Contribute to the continuous improvement of analytics methodologies, tooling, and best practices within DAAIS
• Collaborate with data engineering and platform teams to ensure scalable and reliable analytics solutions

Required Skills & Qualifications

•  
• 3–5 years of relevant work experience within a financial institution, in front office, middle office, or back office functions.

• Strong foundation in statistics, econometrics, applied mathematics, or quantitative finance
• Hands-on experience with quantitative analysis techniques such as regression, time-series analysis, classification, or attribution analysis. Experience on machine learning model (tree based, gradient boosting) is a plus
• Ability to assess model performance, limitations, and business relevance

Programming & Data Skills

• Proficiency in Python (e.g., pandas, numpy, scikit-learn, statsmodels)
• Experience working with large datasets using SQL and modern data platforms
• Familiarity with data cleaning, transformation, and feature engineering best practices

Business & Communication Skills

• Strong ability to connect quantitative results with business context and decision-making needs
• Clear written and verbal communication skills, with experience explaining analytical findings to diverse audiences
• Comfortable working in a cross-functional, global environment

Education & Background

• Bachelor’s or Master’s degree in Quantitative Finance, Statistics, Mathematics, Computer Science, Economics, or a related field
• Prior experience in asset management, financial services, data analytics, or a similar quantitative role is preferred

What Success Looks Like

• High-quality, reliable quantitative analyses that directly inform SSIM business decisions
• Strong partnerships with business and technology stakeholders
• Clear, well-documented analytical outputs that can be reused and scaled across SSIM

About State Street

Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.

We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.

As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.

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