AVP, Data-Driven Risk & Solutions for Front Office

CFA Institute; Singapore; Hybrid; SGD 180,000 - 260,000; Full time

Job summary

GIC is seeking an AVP in the Portfolio Execution & Solutions Group to partner with Front Office stakeholders, translate questions into quantitative models and oversee data-driven analytics projects. You will work with research professionals and portfolio managers, using Python, SQL and AI tools to accelerate model development and backtesting.

Ideal candidates have 6–8 years of asset management experience, strong data skills, and a collaborative mindset, with hybrid work arrangements and

Qualifications

• Bachelor's degree in a quantitative discipline or Finance with strong quantitative content.
• 6–8 years of experience in asset management or related industry with analytics and backtesting experience.
• Strong programming and data skills: Python and SQL; working knowledge of data engineering; Excel proficiency.

Responsibilities

• Define and refine business solutions needs with senior Front Office stakeholders.
• Translate investment questions into quantitative models; design, implement, backtest and validate models.
• Oversee demand management and plan capability roadmaps within PESG.
• Source and structure specialised datasets with data teams to scale research.
• Utilise AI tools to accelerate data preparation, model development and research workflows.
• Communicate complex results to researchers, PMs and Front Office stakeholders.

Skills

Python

SQL

Data visualization

Excel

AI tools usage

Communication

Stakeholder management

Financial market knowledge

Education

Bachelor's degree in quantitative discipline

CFA/FRM preferred

Tools

Tableau

Databricks

Job description

GIC is seeking an AVP in the Portfolio Execution & Solutions Group to partner with Front Office stakeholders, translate questions into quantitative models and oversee data-driven analytics projects. You will work with research professionals and portfolio managers, using Python, SQL and AI tools to accelerate model development and backtesting.

Ideal candidates have 6–8 years of asset management experience, strong data skills, and a collaborative mindset, with hybrid work arrangements and