Founded in 2018, Talos provides institutional-grade trading technology for the global digital asset market, powering many of the major players in the crypto ecosystem.

Our mission at Talos is clear: to advance the mass adoption of digital assets by seamlessly connecting institutions to the digital asset ecosystem. We are committed to building the most innovative and trusted platform in the world, supporting the entire trading lifecycle.

At Talos, you'll find an environment that champions kindness and respect, values diverse perspectives, and upholds inclusivity at every turn. We believe that every member of our team adds invaluable insights and abilities that drive Talos forward. In our pursuit of excellence, we foster a culture of trust and integrity, collaboration, and mutual growth. Together, we are ambitiously building something extraordinary. Your unique talents and insights will play a crucial role in our shared success.

We are a tight-knit but decentralized team of highly-experienced engineers and businesspeople. We have a hybrid-friendly work environment, with physical hubs in New York, London, Singapore, Sweden and Cyprus.

About the Role

As a Quantitative Analyst Intern, you will support our team in analyzing trading and proprietary data, and helping produce quantitative models that drive the decision making behind our quantitative execution offerings. You will assist in producing quantitative signals, impact models, and analyze Talos unique trading datasets to support improvements of the trading products and beyond.

You will work alongside experienced team members, including product owners, quantitative analysts, and managers who will provide guidance and mentorship. This role offers excellent exposure to quantitative trading technology and will provide hands-on experience with financial markets data analysis. You'll gain practical experience while working with experienced professionals in the field.

Want to learn more about Talos’s engineering culture? Check out Sonic, our first open-source project which allows Talos to achieve microsecond network stack latency in Go: https://github.com/talostrading/sonic

Responsibilities and Duties

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

Qualifications

Class of 2026 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 London HQ

Benefits

You will also enjoy a comprehensive array of competitive benefits, regardless of your location, within our warm, welcoming, and ambitious company culture. We provide paid lunches in the office, monthly fitness and evening socials to foster connections with colleagues, and other events to engage with the wider team.

Get In Touch!

Sounds compelling? We’d love to hear from you. Contact us directly.

Also, check out other open positions listed on our website.

Talos is proud to be an Equal Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. Talos is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at careers@talos.com.

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