Internship Quantitative Trading (Market Making) (m/f/x)

Intern

Company Description

Scalable Capital is a leading digital investment and banking platform with a full banking licence, empowering people across Europe to shape their own finances. Scalable Broker makes it easy and affordable for clients to invest professionally in stocks, ETFs, cryptocurrencies, and derivatives, as well as set up savings plans. Scalable Wealth, the digital wealth management service, offers clients professional investment in ETF portfolios, and is also adopted as a white-label solution by banks and other B2B partners. The company’s offerings are rounded off by attractive interest rates, loans, and private equity. With the European Investor Exchange, Scalable Capital offers an exchange specifically for retail investors. Over one million clients have already entrusted more than €30 billion to the platform.

Founded in 2014, Scalable Capital now employs over 700 people across Munich, Berlin, Vienna, Milan, and London. Together with the founding and management team, including Erik Podzuweit and Florian Prucker, they are working on a new generation of financial services.

Visit our or check out our Social Media channels to find out what our Expert Teams have to say.

Our Company Values guide us every day in how we work and collaborate. To learn more about them, you can find our values (English).

Job Description

As an intern in Quantitative Trading, you will immerse yourself in the fast-paced environment of equity trading on Scalable Capital's market making desk. You will gain hands-on trading experience and collaborate with other interns, seasoned traders and (quantitative) financial engineers, learning about market dynamics, algorithmic trading strategies, and risk management.

You gain hands-on trading experience in market making for equities, ETFs (equity and fixed income), and other ETPs, providing liquidity and facilitating efficient trading on the European Investor Exchange (EIX).

You create reports and firmwide market updates.

You participate in committees, providing insights and feedback based on your research.

You leverage your programming skills to analyze market data, automate data processing, and enhance data visualization.

You support the development and optimization of trading systems, including the design, backtesting, and implementation of trading algorithms to enhance quoting and execution strategies, and to adapt to changing market conditions.

You learn and apply risk management practices and ensure regulatory compliance.

You work closely with data teams, quantitative engineers, developers, and seasoned traders.

Qualifications

You are a Bachelor’s, Master’s, or PhD university student, preferably in Finance, Mathematics, Statistics, Computer Science, or a related field.

You demonstrate proficiency in programming languages such as Python and SQL, and have experience using data analysis tools and libraries (such as Pandas and NumPy).

You are proficient in Microsoft Excel (or Google Sheets).

You have a demonstrable interest in financial markets, trading, and investment strategies.

You are able to deliver under pressure, with excellent analytical skills in a fast-paced environment.

You are a collaborative problem-solver with attention to detail, and possess strong communication and teamwork skills.

You are fluent in English.

Additional Information

Be part of one of the fastest-growing and most visible Fintech startups in Europe, creating innovative services that have a substantial impact on the lives of our customers

The ability to work with an international, diverse, inclusive, and ever-growing team that loves creating the best products for our clients

Enjoy an office in the heart of Munich, located in a lively neighborhood and close to the English garden 

All internships are worth the same with us: we also remunerate mandatory internships 

Learn and grow by joining our in-house knowledge sharing sessions

Work productively with the latest hardware and tools

Free subscription to the PRIME+ Broker

Job Location

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