Quantitative Analyst Intern
Talos provides institutional-grade technology and data for trading and managing digital assets across the full investment lifecycle.
Maintainer signals as of 9/2/2026
Funding history
About Talos
Talos is an active institutional digital-asset trading and investment-management technology company. Its platform supports liquidity sourcing, price discovery, trading and execution, settlement, portfolio management, and post-trade analytics through a unified interface. Talos was founded in 2018, launched its live platform in August 2019, and acquired Coin Metrics in 2025.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will analyze proprietary trading data and intraday signals to help produce quantitative execution alphas and impact models. You will create analytics visualizations, develop and evaluate quantitative strategies, organize algorithm-performance data, study market microstructure and execution practices, prepare quantitative materials, and backtest real-world trading features.
Requirements
- Be in the graduating class of 2029 and pursue a master's or doctoral degree in Computer Science or a related field.
- Demonstrate advanced Python proficiency and hands-on experience with data science libraries, especially pandas.
- Understand dataframe architecture for data manipulation, transformation, and analysis.
- Have familiarity with Python-based data visualization tools as a plus.
- Have experience with SQL, including constructing and optimizing complex queries for large datasets.
- Have experience with BigQuery or another cloud-based querying platform as a strong advantage.
- Understand computational finance concepts and quantitative methods used in financial applications.
- Possess strong knowledge of probability distributions, hypothesis testing, and data sampling methods.
- Apply statistical analysis techniques to financial data.
- Understand linear algebra, calculus, and regression analysis.
- Have exposure to machine learning and deep learning methods for sparse data, including common imputation approaches.
- Know portfolio optimization methodologies, particularly Markowitz risk-return models, as a plus.
- Be local to the New York headquarters.
Responsibilities
- Analyze trading data and proprietary intraday signals to produce quantitative execution alphas.
- Create bespoke analytics visualizations that illustrate trading patterns and execution insights.
- Learn and use internal tools and systems for data analysis.
- Develop quantitative strategies that use execution alphas to support trading decisions.
- Gather and organize data related to trading algorithm performance.
- Learn about market microstructure and best execution practices.
- Prepare quantitative content for client meetings, presentations, and academic research reports.
- Backtest real-world trading features with the quantitative strategies function.
Benefits
- USD 25 lunch credit for days worked in the office.
- Evening socials with the office and fellow interns.
- Catered lunches on Tuesdays.
- Office snacks and drinks.
