Evercommerce is a prominent technology company that provides integrated software solutions for service-oriented businesses across various industries. Our cutting-edge platform aims to streamline operations, enhance client engagement, and drive growth for our clients.

As a Data Scientist at Evercommerce, you will leverage your expertise in data analysis, statistical modeling, and machine learning to uncover actionable insights and drive data-driven decision-making. This role requires proficiency in programming languages such as Python or R, a solid understanding of data mining techniques, and experience with big data technologies.

If you're preparing to join the innovative team at Evercommerce, this guide is tailored for you. It will elucidate the interview process, give you a glimpse of some commonly asked questions, and provide valuable tips to help you excel. Dive in with Interview Query to navigate your journey through the Evercommerce Data Scientist interview process.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining EverCommerce as a Data Scientist. Whether you were contacted by an EverCommerce 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 EverCommerce 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 EverCommerce Data Scientist 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 EverCommerce Data Scientist role is conducted through virtual means, including video conferences and screen sharing. Questions in this 1-hour long interview stage may revolve around EverCommerce’s data systems, ETL pipelines, and SQL queries.

In the case of data scientist 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 EverCommerce 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 Scientist role at EverCommerce.

Quick Tips For EverCommerce Data Scientist Interviews

Here are a few tips to help you prepare for your EverCommerce Data Scientist interview:

• Understand EverCommerce’s Products and Services: EverCommerce questions often revolve around their specific business model and product offerings. Being knowledgeable about their suite of services and how you could impact their growth through data science will be crucial.
• Be Analytical and Business-Centric: EverCommerce’s data science interviews assess how well you can provide business-driving insights with data science. Brush up on your knowledge of analytics and how data-driven decisions can impact business strategies.
• Showcase Collaborative Spirit: At its core, EverCommerce values innovation and collaboration. Practice responding to behavioral questions with answers that touch on these core values.

Typically, interviews at Evercommerce vary by role and team, but commonly Data 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: Determine the time complexity.

• Create 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.

• Develop 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, 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 are down 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 be decreasing while job postings remain constant? You observe that the number of job postings per day has remained stable, but the number of applicants has been steadily 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.

• Write a function to calculate sample variance from a list of integers. Create 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] 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.

• Write a function to return the median value of a list in O(1) time and space. 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] Output: median(li) -> 2

• What are the drawbacks of the given student test score data layouts? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, 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 the process by which a random forest generates its forest. Additionally, discuss why one might choose random forest over other algorithms such as 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?

Exploring the Data Scientist position at Evercommerce? For a deep dive into the company, check out our main Evercommerce Interview Guide, where we cover extensive interview questions you might encounter. Discover more about various roles like Software Engineer and Data Analyst, giving you insights into Evercommerce’s unique interview process.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit. Equip yourself with the knowledge, confidence, and strategic guidance you need to conquer every Evercommerce data scientist interview challenge.

Explore all our company interview guides for thorough preparation, and feel free to reach out if you have any questions.

Good luck with your interview!