Job Description:
Quantitative Analyst Intern
Quant Modelling
Assist in analyzing trading data, proprietary intraday signals, to produce quantitative execution alphas
Help create bespoke analytics visualizations using Talos data to illustrate trading patterns and execution insights
Learn to use internal tools and systems for data analysis
Quantitative Execution Algorithms
Work with Talos quants upon quantitative strategies that utilize execution alphas to drive trading decision making
Help gather and organize data related to trading algorithm performance
Learn about market microstructure and best execution practices
Business Support
Assist in preparing quantitative content for client meetings, presentations and academic research reporting
Work with the Quant strategies to backtest real-world trading features for the pleasure of top leading crypto firms in the space
Job Requirements:
Graduating class of 2029 pursuing a Masters or Doctorate degree in Computer Science or related field
Programming Skills: Advanced proficiency in Python, with hands-on experience in data science libraries (especially pandas) and a strong understanding of dataframe architecture for data manipulation, transformation, and analysis. Familiarity with Python-based data visualization tools is a plus.
Database and Query Languages: Experience with SQL, including constructing and optimizing complex queries for large datasets; experience with BigQuery or other cloud-based querying platforms is a strong advantage.
Computational Finance: Exposure to computational finance concepts, with familiarity in using quantitative methods and tools for finance-related applications.
Statistics and Data Analysis: Strong statistical knowledge, including probability distributions, hypothesis testing, and data sampling methods. Ability to apply statistical analysis techniques to analyze financial data.
Mathematics and Machine Learning: Solid foundation in mathematical principles, including linear algebra and calculus, with a focus on regression analysis. Exposure to machine learning and deep learning algorithms and methods in the presence of sparse data, including common imputation approaches. .
Portfolio Optimization: Knowledge of portfolio optimization methodologies, specifically the Markowitz risk-return models is a plus.
Local to New York HQ
Benefits:
You will also enjoy a comprehensive array of competitive benefits, regardless of your location, within our warm, welcoming, and ambitious company culture. Some of our benefits include:
A lunch credit of $25 USD towards lunches for days in-office
Evening socials with the entire office and fellow interns
Other in-office perks: Catered lunches on Tuesdays, snacks, and drinks
