# Platform Thinking: Three Views from Two Sigma Leaders

[Two Sigma](https://yomu.fyi/company/two-sigma) · Emily Majewski · Oct 23, 2025

**Type:** Explainer

## Summary

Two Sigma structures its quantitative investment operations around foundational platforms that balance operational speed with scientific rigor. In data engineering, foundational teams provide both raw and curated datasets using BigQuery, CI/CD-managed transformation pipelines, and reusable data contracts termed ice cubes to serve ninety percent of use cases. Quantitative modeling incorporates open-source large language models trained strictly on point-in-time data to rapidly generate features while preventing temporal leakage. Long-term platform innovation follows an epsilon and omega strategy that couples overarching vision with iterative, learning-focused steps. Platform leadership categorizes artificial intelligence applications into advisory, oracle, operational, and agentic functions designed to enhance human productivity rather than replace researchers.

## Context

Investment management and quantitative research require foundational infrastructure that balances rapid experimentation with scientific rigor while handling evolving technologies and complex data requirements.

## Approach / What changed

Two Sigma relies on a platform-centric model featuring standardized warehouses like BigQuery, data transformations managed via CI/CD, and reusable data contracts alongside raw data access. Additionally, research workflows incorporate open-source LLMs trained on point-in-time data to prevent temporal leakage while categorizing AI into advisory, oracle, operational, and agentic roles.

## Takeaways

- Foundational data engineering shifted to offering both raw and curated data while using reusable 'ice cube' data contracts to satisfy 90% of use cases without building custom solutions.
- To prevent temporal leakage in feature forecasting, modeling teams utilize open-source models trained on point-in-time data so models do not access future information during past prediction evaluations.
- Platform innovation is framed as stacked S-curves guided by an epsilon and omega approach that aligns long-term vision with small, learning-generating steps across four distinct AI roles.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [CI/CD](https://yomu.fyi/topic/ci-cd), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning)

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