Babel Street is an innovative data analytics company that excels in transforming vast amounts of unstructured data into actionable intelligence. Known for its cutting-edge technology and insightful solutions, Babel Street operates in diverse fields ranging from government intelligence to corporate risk management.

As a Research Scientist at Babel Street, you will blend advanced technical skills with a deep understanding of data to drive forward-thinking projects. This role requires proficiency in data analysis, machine learning, and a knack for distilling complex data into meaningful insights.

Thinking of joining Babel Street as a Research Scientist? This guide by Interview Query will help you navigate through the interview process, covering frequently asked questions and providing useful tips. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Babel Street as a Research Scientist. Whether you were contacted by a Babel Street recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences that align with Babel Street’s mission and projects.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from Babel Street’s Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Babel Street Research Scientist hiring manager may stay present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Babel Street Research Scientist role is usually conducted through virtual means, including video conferencing and screen sharing. Questions in this 1-hour long interview stage may revolve around Babel Street’s data systems, predictive analytics, and machine learning models.

In the case of Research Scientist roles, take-home assignments regarding data analysis, model building, and data visualization are often included. Apart from these, your proficiency in hypothesis testing, statistical modeling, and algorithm development may also be assessed during the round.

Depending on the seniority of the position, case studies and real-world problem-solving scenarios may also be assigned.

Onsite Interview Rounds

Following a second recruiter call outlining the next steps, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Babel Street office. Your technical prowess, including programming, machine learning, and analytical capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Research Scientist role at Babel Street.

Quick Tips For Babel Street Research Scientist Interviews

• Understand Babel Street’s Products and Services: Babel Street’s questions are standardized and rely heavily on situational scenarios involving their products. Study Babel Street’s range of products and understand how you could contribute to their improvement.
• Be Data Driven: Babel Street’s interviews assess how well you can provide insights and solutions through data science. Brush up on your knowledge of statistics, machine learning, and data analysis, as these questions can be some of the hardest to solve.
• Align with the Company’s Mission: Babel Street values innovation and mission-driven efforts. Practice responding to behavioral questions with answers that reflect their core values and underline your passion for their field of work.

Typically, interviews at Babel Street vary by role and team, but commonly Research Scientist interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: What's the time complexity?

• Write a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Write a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you suspect anything unusual about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What steps would you take if friend requests on Facebook dropped by 10%? A product manager at Facebook reports a 10% decrease in friend requests. What actions would you take to investigate and address this issue?

• Why might job applications decrease while job postings remain constant on a job board? You observe that the number of job postings per day has remained stable, but the number of applicants has been decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common issues in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• How do you write a function to calculate sample variance? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

• Is there anything fishy about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

• How do you find the median in a list with more than 50% of the same integer in O(1) time? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in (O(1)) computational time and space. Example input: li = [1,2,2]. Example output: median(li) -> 2.

• What are the drawbacks and formatting changes needed for messy datasets? Assume you have data on student test scores in two different layouts (dataset 1 and dataset 2). Identify the drawbacks of the current organization, suggest formatting changes for better analysis, and describe common problems in messy datasets.

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model for this problem? If you proceed with the decision tree, how would you evaluate its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain how a random forest generates its forest of decision trees. Additionally, discuss why you might choose random forest over other algorithms like logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which scenarios would you use a bagging algorithm versus a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to track the accuracy and validity of the model?

If you want more insights about the company, check out our main Babel Street Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Babel Street’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Babel Street research scientist interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!