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3 Ways To Approach Quantitative Analysis Interviews

It’s hard to deny technology’s influence on the finance industry. Tasks once handled by paper and decisions made by market legends are now being automated due to faster computers and better data. The evolution of these technologies and platforms are disrupting the industry entirely—giving way to the rise of quantitative analysis roles.

This trend has not only led to major job growth, but it has also created opportunities for professionals with untraditional backgrounds to break into the industry. “Financial institutions are increasingly turning to STEM professionals from the tech industry for front office roles,” says Victor Tang, a Director within The Execu|Search Group’s Financial Services division, who specializes in quantitative analytics and risk. “The interview process is also becoming more rigorous. Regardless of a candidate’s background, they need to ensure all prospective hires can develop high-level trading strategies using data.”

If you can make it through each interview round, you can look forward to a very rewarding career in quantitative analysis. Here are three things you should be prepared to address in all your quantitative analysis interviews:

Programming languages: In order to build the most sophisticated trading algorithms, strong programming skills are a prerequisite for any role. “Be prepared to discuss your programming skills in all first-round quantitative analysis interviews,” advises Victor. “If you cannot do this with confidence, you will not progress to the next stage of the process.” Today, C++ and Python are the most in-demand programming languages for quantitative analysis roles, but do highlight any additional skills you have mastered.

Statistical concepts and machine learning: During the technical part of quantitative analysis interviews, the employer will test your knowledge of statistical concepts and machine learning (if applicable). “To ensure you can analyze data, hiring managers will ask you statistics, probability, and machine learning questions,” says Victor. “If you lack an understanding of the concepts that are relevant to the specific role, this will raise some major red flags.” Questions can range from general concepts to technical applications, including:

  • What is a confidence interval and why is it useful?
  • If variable x1 has an r square of 1%, x2 has an r square of 1%, what’s the range of r square if we can use both x1 and x2?
  • Given an integer n, you can do 3 operations: n-1, n/2, n/3. What’s the minimum operations you need to do to transform n to 1?

Your thought process: If you make it to the final round of the process, the employer will want to evaluate how you put your knowledge into practice. “You might feel like you are back in school during this part of the interview,” says Victor. “It’s difficult to prepare for the specific question, but they will most likely provide you with a set of raw data that you will have to clean, process, and analyze in order to reach the correct answer.”  Some sample questions you might encounter include:

  • There are 30 blue and 30 red balls and two urns. You play a game. Your opponent has the right to arrange the balls in the two urns as he pleases, without telling you what he did. You then must draw a ball from an urn of your choice. You win $10 if you draw a blue ball, otherwise you get $0. How much would you be willing to pay to play this game?
  • There are 26 black cards and 26 red cards, you chose a color, and start flipping cards one by one. You can call stop any time. If you call stop and the next card’s color is your chosen color, you win. What is the winning strategy?