At Susquehanna International Group (SIG), data is not just an asset—it is the lifeblood of the entire firm. As a Data Scientist, you will operate at the intersection of quantitative research, software engineering, and live trading. Unlike traditional technology companies where data science might focus on user growth or product features, at SIG, your work directly impacts financial market-making, algorithmic execution, and risk management. You will build, test, and deploy highly sophisticated predictive models that analyze massive, high-frequency market datasets to identify pricing anomalies and execute trades in fractions of a second.

This role is highly critical because SIG is a leading global proprietary trading firm. The models you build will directly influence how capital is allocated across options, equities, derivatives, and other complex financial instruments. You will collaborate closely with quantitative researchers, traders, and systems engineers to translate mathematical theories into highly optimized, real-world trading strategies.

The environment is fast-paced, intellectually rigorous, and deeply collaborative. Whether you are optimizing existing option-pricing models or exploring alternative datasets to find novel signals, your day-to-day work will involve solving some of the most complex mathematical and computational challenges in the financial industry. For individuals who thrive on immediate feedback, quantitative rigor, and high-impact problem-solving, this role offers an unparalleled opportunity to see your work directly affect the firm's bottom line.

The following questions are representative of what you will face during the Susquehanna International Group interview process. These questions are drawn from real candidate experiences and are designed to test your core mathematical capabilities, quick decision-making, and logical reasoning under intense time pressure.

Probability & Combinatorics

As a quantitative firm, SIG places immense weight on your mastery of probability theory. You must be able to perform rapid calculations and structure your thoughts clearly.

• What is the probability of getting a Head-Tail-Head (HTH) sequence before a Head-Head-Tail (HHT) sequence when flipping a fair coin repeatedly?
• You are flipping a fair coin. What is the expected number of flips required to get two consecutive heads?

To succeed in the SIG Data Scientist interview process, you must shift your mindset from standard tech-company data science prep to highly rigorous quantitative finance prep. SIG cares deeply about your fundamental mathematical capabilities and your speed of execution.

Mathematical Rigor & Speed – You must be exceptionally fast and accurate with mental math, probability, and combinatorics. The firm values candidates who can calculate expected values, conditional probabilities, and permutations instantly without relying on calculators or writing down extensive notes.

Structured Problem-Solving – When faced with a complex brain teaser or probability puzzle, your interviewer wants to hear how you think. You should explicitly state your assumptions, break the problem down into smaller components, and explain your logical progression step-by-step.

Game-Theoretic Mindset – SIG has a strong gaming culture (poker, chess, and board games are highly celebrated). You must demonstrate an understanding of risk management, expected value, bankroll management, and competitive decision-making.

Cultural Alignment & Motivation – You need a compelling answer for why you want to work at SIG specifically, and why you are drawn to the trading floor environment. Showing a passion for competitive games, market dynamics, and fast feedback loops is highly valued.

The interview process for a Data Scientist at Susquehanna International Group is highly structured, rigorous, and typically spans 4 to 8 weeks. It is designed to filter for exceptional mathematical talent and quick decision-making capabilities.

The journey begins with an automated Online Assessment (OA), which serves as a strict filter. Candidates who pass the OA move on to a recruiter phone screen, followed by multiple rounds of intense technical interviews with quantitative researchers, traders, and software engineers. The process often includes an open-ended take-home data exercise before culminating in a multi-round superday.

The visual timeline above outlines the typical progression of a candidate through the SIG recruiting pipeline. It starts with the initial screening phase (OA and recruiter call), transitions into deep-dive technical evaluations, and concludes with a comprehensive onsite superday. Candidates should expect a consistent focus on mathematics and probability at every single stage of this process.

Probability and Statistics

This is the most critical evaluation area in the entire SIG interview process. You will be tested on these concepts in the online assessment, the recruiter phone screen, and every subsequent technical interview.

Be ready to go over:

• Conditional Probability & Bayes' Theorem – Calculating updated probabilities as new information becomes available, particularly in sequential game or coin-tossing scenarios.
• Expected Value & Variance – Finding the expected outcome of complex games, betting structures, or random walks.
• Combinatorics – Permutations, combinations, and partition problems under strict time constraints.
• Advanced Stochastic Concepts – Basic understanding of martingales, Markov chains, and random walks.

Example questions or scenarios:

• "You roll a die repeatedly. What is the expected sum of the rolls when you stop after rolling a 6?"
• "What is the probability that a random walk on a 1D integer grid, starting at 0, reaches +3 before reaching -5?"

Quantitative Finance & Decision-Making

At SIG, data science is intimately tied to trading. Interviewers will evaluate how you apply mathematical principles to financial decision-making, even if you do not have a prior background in finance.

Be ready to go over:

• Market Making Games – Simulating a trading environment where you must quote bid and ask prices for an unknown quantity and adjust your quotes based on new information.
• The Kelly Criterion – Understanding how to size bets or positions based on your edge and the odds of the game.
• Option Pricing Intuition – Basic concepts of derivatives pricing, delta hedging, and how volatility affects option value.

Example questions or scenarios:

• "Make a market on the number of home runs hit in the Major League Baseball season this year. What is your bid, what is your ask, and how do you adjust them if I buy from you?"
• "You are offered a lottery ticket that pays $100 with a 10% probability. What is the maximum price you would pay for this ticket, and how does your risk tolerance change if you can buy 1,000 of these tickets?"

Coding & Data Analysis

While math is the primary focus, you must also demonstrate strong computational skills to handle the massive datasets generated by financial markets.

Be ready to go over:

• Python Proficiency – Writing clean, efficient code to manipulate data, simulate games, or implement mathematical models.
• Algorithmic Complexity – Understanding time and space complexity (Big O notation) to ensure your code can run in high-frequency trading environments.
• Take-Home Data Exercise – An open-ended project where you are given a dataset and must build a predictive model or trading strategy and justify your design decisions.