Tower Research Capital Research Scientist Interview Questions + Guide in 2025

Tower Research Capital is a leading global trading firm leveraging technology and quantitative research to drive its trading strategies.

The Research Scientist role at Tower Research Capital involves the application of advanced quantitative analysis, algorithm development, and data-driven methodologies to inform trading strategies. Key responsibilities include developing and optimizing complex algorithms, performing statistical analysis, and conducting rigorous research to enhance trading performance. Candidates should possess strong programming skills, particularly in languages such as Python and C++, alongside a solid understanding of algorithms, data structures, and quantitative finance principles. Additionally, familiarity with statistical methods and probability is critical in this role.

Success in this position requires a strong analytical mindset, attention to detail, and the ability to work effectively in high-pressure situations, aligning with Tower Research's commitment to innovation and excellence in the fast-paced trading environment.

This guide will help you prepare for your interview by providing insights into the skills and knowledge areas that are crucial for success as a Research Scientist at Tower Research Capital.

The interview process for a Research Scientist at Tower Research Capital is structured and thorough, reflecting the company's emphasis on technical expertise and problem-solving skills.

1. Application and Initial Screening

The process begins with an online application, where candidates submit their resumes. Following this, candidates may receive a coding challenge, typically hosted on platforms like HackerRank. This assessment usually consists of multiple coding problems that test algorithms and data structures, with a focus on medium to hard difficulty levels. Candidates are advised to manage their time effectively during this stage, as the challenge can be quite demanding.

2. Technical Phone Interviews

Successful candidates from the initial screening are invited to participate in one or more technical phone interviews. These interviews generally last around 45 minutes to an hour and cover a range of topics, including programming languages (especially Python and C++), algorithms, and data structures. Interviewers may ask candidates to solve coding problems in real-time, requiring them to articulate their thought processes clearly. Additionally, candidates should be prepared for questions related to their past projects and experiences, as well as some behavioral questions to assess cultural fit.

3. Onsite Interviews

Candidates who perform well in the phone interviews are typically invited for onsite interviews. This stage can involve multiple rounds, often lasting a full day. During the onsite interviews, candidates may face a series of technical assessments, including coding tests, system design questions, and discussions on computer architecture and low-latency trading systems. Interviewers may also delve into advanced topics such as probability, statistics, and machine learning, depending on the candidate's background.

4. Final Evaluation

The final stage of the interview process often includes meetings with senior team members or directors. This may involve a mix of technical discussions and behavioral interviews, where candidates are evaluated on their problem-solving abilities, teamwork, and alignment with the company's values. Candidates should be prepared to discuss their motivations for joining Tower Research Capital and how they can contribute to the team.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during this rigorous process.

Here are some tips to help you excel in your interview.

Understand the Technical Landscape

As a Research Scientist at Tower Research Capital, you will be expected to have a strong grasp of algorithms, particularly in the context of high-frequency trading. Brush up on your knowledge of data structures, algorithm complexity, and optimization techniques. Familiarize yourself with common algorithmic problems, especially those related to dynamic programming and graph theory, as these are frequently discussed in interviews.

Prepare for Coding Assessments

Expect to face coding challenges that may include a mix of medium to hard-level questions. Practice coding on platforms like HackerRank, focusing on problems that require efficient solutions. Pay attention to time complexity and space complexity in your solutions, as interviewers will likely probe your understanding of these concepts. Be prepared to write clean, efficient code and explain your thought process clearly.

Master the Basics of Computer Science

A solid foundation in computer science fundamentals is crucial. Review key topics such as operating systems, networking, and database management. Be ready to discuss concepts like TCP/IP, virtual memory, and cache hierarchies. Interviewers may ask you to explain these concepts in detail, so ensure you can articulate your understanding clearly.

Be Ready for Behavioral Questions

While technical skills are paramount, behavioral questions will also play a significant role in your interview. Prepare to discuss your past experiences, projects, and why you want to work at Tower Research Capital. Reflect on your motivations for transitioning to this role and how your background aligns with the company’s goals. Authenticity and enthusiasm can set you apart.

Engage with the Interviewers

During the interview, engage with your interviewers by asking insightful questions about the team, projects, and company culture. This demonstrates your interest in the role and helps you gauge if the company is a good fit for you. Be personable and show that you are not just a candidate, but someone who is genuinely interested in contributing to the team.

Practice Problem-Solving Under Pressure

Expect brainteaser-style questions that test your problem-solving abilities. Practice thinking aloud as you work through these problems, as interviewers often appreciate seeing your thought process. This can also help you manage your time effectively during the interview, especially when faced with challenging questions.

Follow Up Professionally

After your interview, consider sending a thank-you email to express your appreciation for the opportunity. This not only shows professionalism but also reinforces your interest in the position. Mention specific topics discussed during the interview to personalize your message.

By following these tips and preparing thoroughly, you can approach your interview with confidence and increase your chances of success at Tower Research Capital. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Research Scientist interview at Tower Research Capital. The interview process will likely focus on your technical skills, particularly in algorithms, programming (especially Python and SQL), and your understanding of statistics and probability. Be prepared to demonstrate your problem-solving abilities and your knowledge of computer systems and design.

Algorithms

1. Can you explain the difference between a stack and a queue?

Understanding data structures is crucial for this role, and being able to articulate the differences will showcase your foundational knowledge.

How to Answer

Discuss the definitions of both data structures, their use cases, and how they handle data differently.

Example

“A stack is a Last In First Out (LIFO) structure, where the last element added is the first to be removed. In contrast, a queue operates on a First In First Out (FIFO) basis, where the first element added is the first to be removed. Stacks are often used in scenarios like function call management, while queues are used in scheduling tasks.”

2. Describe a dynamic programming problem you have solved.

Dynamic programming is a key concept in algorithm design, and interviewers will want to see your practical experience.

How to Answer

Choose a specific problem, explain the approach you took, and highlight the efficiency of your solution.

Example

“I worked on the 'Knapsack Problem' where I used dynamic programming to optimize the selection of items based on weight and value. I created a 2D array to store the maximum value at each weight limit, iterating through the items and updating the array based on whether to include each item or not.”

3. How would you find the shortest path in a graph?

Graph algorithms are fundamental in many applications, and this question tests your understanding of graph theory.

How to Answer

Discuss algorithms like Dijkstra’s or A* and when to use them.

Example

“To find the shortest path in a graph, I would typically use Dijkstra’s algorithm, which efficiently finds the shortest path from a source node to all other nodes. It uses a priority queue to explore the nearest unvisited node and updates the path lengths accordingly.”

4. Explain the concept of recursion and provide an example.

Recursion is a common technique in programming, and understanding it is essential for solving complex problems.

How to Answer

Define recursion and provide a simple example to illustrate your point.

Example

“Recursion is a method where a function calls itself to solve smaller instances of the same problem. For example, calculating the factorial of a number can be done recursively by multiplying the number by the factorial of the number minus one until reaching one.”

5. What is the time complexity of your favorite sorting algorithm?

Sorting algorithms are fundamental in computer science, and knowing their complexities is crucial.

How to Answer

Discuss your preferred sorting algorithm and its time complexity in different scenarios.

Example

“My favorite sorting algorithm is QuickSort, which has an average time complexity of O(n log n). However, in the worst case, it can degrade to O(n^2) if the pivot selection is poor. I prefer it for its efficiency and in-place sorting capabilities.”

Programming (Python & SQL)

1. How do you handle exceptions in Python?

Exception handling is a critical aspect of robust programming.

How to Answer

Explain the try-except block and provide an example of its use.

Example

“In Python, I handle exceptions using try-except blocks. For instance, when reading a file, I would wrap the file operation in a try block and catch any IOError exceptions to handle cases where the file might not exist.”

2. Write a SQL query to find the second highest salary from a table.

SQL skills are essential for data manipulation and retrieval.

How to Answer

Provide a clear SQL query and explain your logic.

Example

“To find the second highest salary, I would use the following query: SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees); This retrieves the maximum salary that is less than the highest salary.”

3. What are the differences between INNER JOIN and LEFT JOIN in SQL?

Understanding joins is crucial for database management.

How to Answer

Define both types of joins and explain their differences with examples.

Example

“An INNER JOIN returns only the rows that have matching values in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. If there’s no match, NULL values are returned for columns from the right table.”

4. How would you optimize a slow SQL query?

Performance optimization is key in data-heavy roles.

How to Answer

Discuss indexing, query structure, and other optimization techniques.

Example

“To optimize a slow SQL query, I would first analyze the execution plan to identify bottlenecks. Then, I would consider adding indexes on columns used in WHERE clauses, simplifying the query structure, and avoiding SELECT * to reduce the amount of data processed.”

5. Can you explain the concept of normalization in databases?

Normalization is essential for database design and efficiency.

How to Answer

Define normalization and its importance in database management.

Example

“Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller ones and defining relationships between them, typically following normal forms to ensure efficient data storage.”

Statistics & Probability

1. What is the Central Limit Theorem?

Understanding statistical concepts is vital for data analysis.

How to Answer

Explain the theorem and its significance in statistics.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the original distribution of the data. This is significant because it allows us to make inferences about population parameters using sample statistics.”

2. How do you calculate the probability of an event?

Basic probability calculations are fundamental in data science.

How to Answer

Discuss the formula for probability and provide an example.

Example

“The probability of an event is calculated as the number of favorable outcomes divided by the total number of possible outcomes. For example, the probability of rolling a three on a six-sided die is 1/6, since there is one favorable outcome and six possible outcomes.”

3. Explain the difference between Type I and Type II errors.

Understanding errors in hypothesis testing is crucial for research.

How to Answer

Define both types of errors and their implications.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. Type I errors are often considered more serious in many contexts, as they can lead to false claims of significance.”

4. What is a p-value?

P-values are essential in hypothesis testing.

How to Answer

Define p-value and its role in statistical analysis.

Example

“A p-value is the probability of obtaining results at least as extreme as the observed results, assuming that the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis, leading to its rejection.”

5. How would you approach a problem involving Bayesian statistics?

Bayesian methods are increasingly important in data analysis.

How to Answer

Discuss the principles of Bayesian statistics and how you would apply them.

Example

“I would start by defining the prior distribution based on existing knowledge or beliefs about the parameters. Then, I would collect data to update this prior using Bayes’ theorem, resulting in a posterior distribution that reflects both prior beliefs and new evidence.”