Commodities Research Associate

Bridgewater Associates•Singapore, NY

2h•$150,000 - $200,000•Hybrid

About The Position

This role sits in Bridgewater's Asia Strategies department, specifically within the Asia Commodities team. The team is responsible for generating alpha and managing risk in commodities, with their research feeding directly into the commodities strategy for Bridgewater's largest funds in the region. The position emphasizes direct ownership of vendor data relationships and quality, research-adjacent problem-solving, data operations across the full research cycle, and integration within the investment process. The individual will be expected to drive their own agenda, be entrepreneurial, resourceful, and build serious infrastructure while staying close to live trading decisions.

Requirements

2–4 years of experience in a data engineering, data science, or analyst role at a commodity trading house, hedge fund, or research organization with exposure to commodities markets.
Familiarity with metals fundamentals and commodities market data preferred. Additional experience with other commodities (e.g. Ags, PetChem) would be a plus.
Experience with systematic commodities trading strongly preferred.
Strong programming skills in Python and SQL preferred. Experience writing production-quality, maintainable code.
Experience working with modern data platforms (e.g. Snowflake or similar cloud data warehouses).
Willingness to expand toolkit to adapt to our scala based production stack.
Strong desire to work with new development assistant technologies to drive productivity.
Experience designing, documenting, and scaling commodities data and trading systems.
Solid foundation in statistics, particularly time-series and cross-sectional analysis.
Mandarin proficiency strongly preferred.
Humility, innate curiosity, and openness to new ideas and approaches.
Driven, confident, and goal-oriented.
Honest, exceptionally direct, and eager to provide and receive objective feedback.
Commitment to self-improvement through feedback and self-reflection.
Commitment to the pursuit of excellence.

Nice To Haves

Additional experience with other commodities (e.g. Ags, PetChem) would be a plus.
Mandarin proficiency strongly preferred.

Responsibilities

Source and maintain vendor data feeds, working directly with vendors to resolve data quality issues and communicate new features.
Transform granular, contract-level market data into derived analytical constructs — curves, spreads, aggregated indicators — that feed directly into research and portfolio decisions.
Build the data infrastructure that feeds fundamental supply/demand balance models — from raw inventory, production, and shipment data to balance-ready inputs.
Build and maintain dashboards and monitoring tools the team relies on daily — tracking both data health and market conditions, and flagging breaks or shifts before they affect the book.
Partner closely with investors and developers to ensure data is research-ready, well-documented, and reproducible across simulation and live environments.
Design, build, and maintain robust, scalable data pipelines supporting systematic commodities strategies.
Interrogate datasets to understand distributions, coverage gaps, stability over time, and structural breaks.
Design and maintain our data ontology and schemas.
Work across a variety of datasets ranging from traditional datasets to large alternative datasets.
Work with shared data engineering and platform teams to evolve the broader data ecosystem while maintain team-level ownership and agility.
Contribute to improvements in tooling, standards, and best practices that increase research velocity and system reliability.
Partner with our investors to research questions on commodity markets and commodity-producing companies, with a specific focus on Asia and Chinese commodities markets, and turn that understanding into systematic trading strategies.
Contribute fundamental context to data decisions – flagging when a data quality issue or vendor discrepancy has a market explanation worth surfacing to the research team.
Incorporate AI/ML technologies into the team's research and data infrastructure.