Popular Bank stands out as a reputable institution with a broad range of financial services, priding itself on customer-centric solutions and innovative banking practices. The bank is recognized for its commitment to excellence and community presence.

A Data Analyst position at Popular Bank is a role that demands a high level of technical proficiency and analytical acumen. As a Data Analyst, you will be expected to leverage your skills in data manipulation, statistical analysis, and visualization to unearth valuable insights. This role plays a crucial part in driving data-informed decisions and optimizing processes throughout the bank.

To help you navigate the interview process, Interview Query provides comprehensive resources and guidance tailored to the Popular Bank Data Analyst role. This guide will prepare you with insights into the interview stages, commonly asked questions, and key pointers to excel. Dive in and let’s help you secure your next career milestone!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Popular Bank as a data analyst. Whether you were contacted by a Popular Bank 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.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Popular Bank 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 Popular Bank data analyst hiring manager stays 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 Popular Bank data analyst role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Popular Bank’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, 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 Popular Bank office. Your technical prowess, including programming and ML modeling 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 data analyst role at Popular Bank.

Quick Tips For Popular Bank Data Analyst Interviews

• Understand Popular Bank’s Products and Services: Be familiar with Popular Bank’s offerings and how data analytics can help improve their services. Tailor your study of analytics to scenarios relevant to the banking sector.
• Be Proficient in SQL and Data Manipulation: Popular Bank uses SQL extensively, so ensure you practice and can confidently write complex SQL queries. Practice data manipulation exercises through platforms like Interview Query.
• Highlight Team Collaboration and Communication Skills: Popular Bank values a collaborative work environment. Demonstrate your ability to work in teams and communicate complex data insights clearly.

Typically, interviews at Popular Bank vary by role and team, but commonly Data Analyst interviews follow a fairly standardized process across these question topics.

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

• What are the drawbacks of the given student test score datasets, and how would you reformat them? Analyze the provided student test score datasets for potential issues. Suggest formatting changes to make the data more useful for analysis. Also, describe common problems in "messy" datasets.

• What metrics would you use to determine the value of each marketing channel? Given the marketing costs for different channels at a B2B analytics company, identify the metrics you would use to evaluate the value of each marketing channel.

• How would you determine the next partner card using customer spending data? Using customer spending data, outline the process to identify the best partner for a new credit card offering.

• How would you investigate if the redesigned email campaign led to the increase in conversion rates? Given the fluctuating conversion rates before and after a new email campaign, describe how you would determine if the campaign caused the increase or if other factors were involved.

• Write a function search_list to check if a target value is in a linked list. Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with keys value and next. If the linked list is empty, you'll receive None.

• Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product. Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

• Create a function digit_accumulator to sum every digit in a string representing a floating-point number. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

• Develop a function to parse the most frequent words used in poems. You're hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

• Write a function rectangle_overlap to determine if two rectangles overlap. You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

• How would you design a function to detect anomalies in univariate and bivariate datasets? If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

• What are the drawbacks of the given student test score data layouts? Assume you have data on student test scores in two layouts. What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

• What is the expected churn rate in March for customers who bought subscriptions since January 1st? You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

• How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• What are Z and t-tests, and when should you use each? What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

• How does random forest generate the forest and why use it over logistic regression? Explain how random forest creates multiple decision trees and combines their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare the use cases for bagging and boosting algorithms. Provide examples of tradeoffs, such as bagging reducing variance and boosting improving accuracy but being more prone to overfitting.

• How would you evaluate and compare two credit risk models for personal loans?

• Identify the type of model developed by the co-worker for loan approval.
• Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installments.

• List metrics to track the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

• What’s the difference between Lasso and Ridge Regression? Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

What is the average salary for a Data Analyst at Popular Bank?

Q: What is the interview process like for a Data Analyst position at Popular Bank?
The interview process typically involves multiple stages, including an initial phone screening, a technical interview, and an onsite interview. Each stage is designed to assess different aspects of your skills, such as your technical proficiency, problem-solving abilities, and cultural fit with the company.

Q: What skills are required to be a successful Data Analyst at Popular Bank?
To be successful in this role, you should have strong analytical skills, proficiency in data manipulation and visualization tools like SQL, Excel, and Tableau, as well as experience with statistical software such as R or Python. Strong communication skills and the ability to translate complex data into actionable insights are also crucial.

Q: What kind of technical questions can I expect during the interview?
You can expect questions that assess your understanding of data analysis techniques, SQL queries, data cleaning, and statistical methods. Be prepared to work on case studies or real-world scenarios that demonstrate your ability to analyze data and provide actionable insights.

Q: What is the company culture like at Popular Bank?
Popular Bank fosters a collaborative and inclusive work environment. The company values innovation, teamwork, and continuous learning. Employees are encouraged to take initiative and contribute to impactful projects.

Q: How can I best prepare for an interview at Popular Bank?
To prepare for your interview, make sure to review your technical skills, particularly in data analysis and visualization. Practice common interview questions and work on case studies to hone your problem-solving abilities. Utilize resources like Interview Query to practice and perfect your interview skills.

If you want more insights about the company, check out our main Popular Bank 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 Popular Bank'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 Popular Bank data analyst 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!