Zilliant is a leading provider of intelligent B2B pricing, sales, and revenue operations and intelligence software. They leverage AI and machine learning to help businesses optimize pricing strategies and drive profitable growth.

A Data Analyst position at Zilliant entails a blend of robust data analysis, statistical modeling, and strategic insights to support data-driven decision-making. Candidates are expected to possess strong skills in data manipulation, statistical tools, and have a good understanding of business pricing models.

If you aim to be a part of this dynamic company and excel as a Data Analyst, Interview Query is here to guide you. In this guide, we will dive into the interview process, explore common questions, and offer valuable tips to help you succeed. Let’s get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Zilliant as a data analyst. Whether you were contacted by a Zilliant 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 Zilliant 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 Zilliant 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 Zilliant 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 Zilliant’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 Zilliant office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalists 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 Zilliant.

Quick Tips For Zilliant Data Analyst Interviews

• Brush Up on SQL and ETL Skills: Zilliant places a strong emphasis on data systems. Make sure you are comfortable with SQL queries and extracting, transforming, and loading data.
• Be Prepared for Technical Rounds: Focus on hypothesis testing, probability distributions, and basic machine learning concepts. Practice solving problems that are related to real-world scenarios and case studies.
• Understand Zilliant’s Products: Have a deep understanding of Zilliant’s product offerings and how your role as a data analyst would contribute to their business. Be ready to discuss ways to improve or test their products with your analytical skills.

Typically, interviews at Zilliant vary by role and team, but commonly Data Analyst 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 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. Based on this outcome, determine if the coin is fair.

• Write a function to calculate sample variance from a list of integers. Create a function that takes a list of integers and returns the sample variance, rounded 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 suspicious 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 to find the median in a list with more than 50% of the same integer in O(1) time and space? Given a sorted list of integers where more than 50% of the list is the same 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? You have student test scores in two different layouts (dataset 1 and dataset 2). 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?

What is the average salary for a Data Analyst at Zilliant?

Q: What is the interview process for a Data Analyst position at Zilliant?
The interview process at Zilliant typically includes an initial phone screen, followed by a technical assessment, and then onsite or video interviews. The stages are designed to evaluate your analytical skills, technical proficiency, and cultural fit with the company.

Q: What skills are crucial for a Data Analyst at Zilliant?
To be successful as a Data Analyst at Zilliant, you should have strong analytical and problem-solving skills, proficiency in SQL and statistical tools, and experience with data visualization software. Familiarity with Python or R can be a plus.

Q: What kind of projects do Data Analysts at Zilliant work on?
Data Analysts at Zilliant work on a range of projects, including data modeling, analysis of customer data to identify trends, and generating actionable insights. They contribute to improving business strategies and optimizing pricing models.

Q: How is the company culture at Zilliant?
Zilliant fosters an innovative and collaborative culture. The team values creativity, diversity, and continuous learning. Employees often work in cross-functional teams to tackle complex challenges and drive growth.

Q: How can I prepare for a Data Analyst interview at Zilliant?
To prepare for an interview at Zilliant, you should familiarize yourself with the company’s products and services. Practice common data analysis problems on Interview Query, review SQL and statistical concepts, and be ready to discuss your previous projects and experiences.

Embarking on a journey with Zilliant as a Data Analyst promises a dynamic and challenging career path. If you want more insights about the company, check out our main Zilliant 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 Zilliant’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 Zilliant 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!