American Express Data Scientist jobs and interview questions

  • AmEx plans to hire 20 data scientists in the next six to 12 months.
  • New hires will work on a machine learning setup which is one of the largest in the financial industry.
  • Interviewers want candidates who demonstrate skill and learning agility in problem solving.

If I have two children and one child is a girl, what is the probability that both children are girls?

For most people, this question is probably nonsense. But if it seems obvious, you might have what it takes to fill one of the 20 data scientist positions American Express is hiring over the next six to 12 months.

“Solving this problem is no different than how we build a machine learning model and build the mathematical model to solve a real problem,” said Di Xu, vice president of AI labs and governance of AI at AmEx.

AmEx’s push for data scientists follows an increase in industry-wide demand for similar roles. According to the Bureau of Labor Statistics, employment in data science and related occupations is expected to increase by 31.4% from 2020 to 2030. And AmEx’s own transition over the past decade of statistical learning models traditional to machine learning avenues with a wider use of AI in business processes.

AmEx began investing in AI in 2010, according to Anjali Dewan, vice president of consumer marketing and business personalization decision science, in hopes of seeing if the model would better help address the few. eight billion company transactions.

It did, and as of 2014, 100% of the company’s credit and fraud stakeholders have switched to AI.

When interviewing for data scientist positions, Xu said interviewers try to understand a candidate’s skills, learning agility and adaptability, in order to make AI smarter and more efficient.

Interviews are divided into two sections to test technical skills and behavioral skills. Xu said the questions are more open-ended so that interviewers can observe how the candidate goes through the problem-solving process.

“The final answer, whether it is right or wrong, matters less,” Xu said. “We care more about the candidate’s problem-solving process. “

Some questions don’t have definitive answers, but those that do can be answered in different ways, Xu said. “We can design additional questions along the way to probe how the candidate will frame the problem and approach the question.”

For example, candidates usually start by answering a simpler question, like the one mentioned above:

If I have two children and one child is a girl, what is the probability that both children are girls?

Some candidates will approach this probability-based question using mathematical theory to find a formula; or they can use computer code that can solve the problem. But Xu said the process is more complex.

“There is a way for people to phrase this problem very concisely and simply,” Xu said. “If they can do it, it’s no different creating a little mathematical model around the problem.”

The next step of this question adds another dimension: If I have two children, one of them is a girl born on a Tuesday. What is the probability that both children are girls?

If the candidates can correctly phrase this more complex problem, Xu said it would help to understand how they approach the problems.

Other prompts in the interview process are more hypothetical, like this one: Design some kind of data product for a restaurant – we need to know the prices of the entrees on the menu of a given restaurant. How do we use transactional data to infer this?

Transactional data is among the most important data of AmEx, said Xu, because it can display a lot of information and allows AmEx to continue to innovate and improve the customer experience.

Xu said he didn’t know the perfect answers to the hypothetical questions himself – instead he wanted to see what the candidates came up with.

“They can give us ideas, and maybe the perfect answer can come out of that process,” he said.

In the behavioral portion of the interview, Dewan said interviewers wanted to see if candidates had leadership skills. Candidates should be effective collaborators, working with different groups to better understand the issues.

According to Dewan and Xu, what matters most is how quickly a candidate learns and their willingness to improve.

“Competence is only one aspect, but learning agility is the other aspect,” said Xu. “It’s very critical for our future success.”

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