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# Data Scientist - Modeling

- **Company:** Two Sigma Investments, LLC.
- **Location:** United States (Remote available)
- **Salary:** $165,000.0
- **Contract:** Permanent contract
- **Skills:** Automation of Tests, High-Frequency Trading, Large Language Models
- **Published:** June 8, 2026
- **Apply:** https://www.dice.com/job-detail/0d0fc89c-21ec-4d00-b0be-96f7dd29f46a

## About the Role

* Good understanding and intuitions about intraday options markets data -- OPRA, NOTO, PHOTO, PHILX, open-close options data
 * Experience building LLM-based featurization pipelines -- including iterative improvement, automated testing, and productionization
 * Experience with time-series data, financial datasets, or quantitative research
 * Ability to work independently on ambiguous research problems while delivering production-quality output
 * Experience working with high frequency trading platforms and infrastructure
 * Strong communication skills - ability to present at forums like FeatureCon and collaborate across modeling and engineering

## Description

Intraday Feature Development

 * Design and implement features from intraday options feeds, microstructure and order book, etc.
 * Serve as Intraday Dynamics' expert on vol markets. Produce frequently updating features for spot trading based on data from these markets
 * Leverage modern AI to maximum effect at every stage of the feature development process
 * Rigorous QA including lookahead detection, coverage analysis, and vendor data reconciliation
 * Collaborate with modelers on the team to evaluate predictiveness and integrate features into production models

LLM-Based Featurization

 * Participate in the Intraday Dynamics team's focus on LLM-based featurization and agentic workflows. Share skills, insights, etc. with team mates
 * Scale existing LLM featurization pipelines to new intraday datasets - esp. OPRA and microstructure
 * Design prompting strategies and directives to generate domain-informed features at varying complexity levels
 * Build evaluation frameworks for LLM-generated features (normalization, residualization, learner-based testing)


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