Zilliant powers intelligent B2B commerce with industry-leading price optimization and sales guidance software. Our data science and cloud-native solutions deliver the highest ROI and customer satisfaction. We are seeking a Data Scientist to join our team and make an impact at a growing company that values innovation and collaboration.

As a Data Scientist at Zilliant, you will create and maintain advanced models, engage in exploratory data analysis, and act as a Pricing Science expert for clients. You’ll report to the Director of Science and play a key role in shaping our core products.

In this guide, Interview Query will walk you through the interview process, common questions, and valuable tips for the Zilliant Data Scientist role. Ready to get started? 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 Zilliant as a Data Scientist. 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 is 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 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 Zilliant data scientist 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 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 Zilliant 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 Zilliant.

Quick Tips For Zilliant Data Scientist Interviews

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Zilliant interview include:

• Understand Pricing Strategies: Zilliant solutions center around pricing strategies. Be familiar with different pricing models like cost-plus pricing, value-based pricing, and dynamic pricing.
• Strong Analytical Skills: Brush up on your data analysis and statistical skills. Be prepared to analyze complex datasets and draw actionable insights.
• Clear Communication: Demonstrate your ability to explain complex technical topics in simple terms. Ensuring that stakeholders understand the business implications of data science is crucial at Zilliant.

Typically, interviews at Zilliant 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 of your solution.

• 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. The solution should have a complexity of \(O(n)\).

• Develop a function precision_recall to calculate precision and recall metrics. 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. Write a function to search for a target value in the array and return its index; otherwise, return -1. The 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 the number of job applicants be decreasing while job postings remain constant? You observe that job postings per day have 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. 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 outputs the sample variance given a list of integers. Round the result to 2 decimal places.

• Would you trust the results of an A/B test with 20 variants if one is significant? Your manager ran an A/B test with 20 different variants and found one significant result. Would you find this result suspicious?

• How to find the median of a list with more than 50% of the same integer in O(1) time and space? Given a list of sorted 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.

• What are the drawbacks of the given student test score data layouts, and how would you reformat them? 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 whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice for this problem?

• How would you evaluate the performance of a decision tree model before and after deployment? If you decide to use a decision tree model, how would you assess its performance both before deployment and after it is in use?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss why one might choose random forest over logistic regression for certain problems.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which scenarios would you prefer a bagging algorithm over 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? If your manager asks you to build a neural network model to solve a business problem, how would you justify the complexity of the model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? Assume you have built a V1 of a spam classifier for emails. What metrics would you use to monitor the model's accuracy and validity?

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

Q: What is the role of a Data Scientist at Zilliant? As a Data Scientist at Zilliant, you'll be responsible for creating and maintaining advanced scientific models that fuel our core products. You'll also act as a Pricing Science expert, advising Zilliant customers on solutions to both simple and complex problems. Your tasks will include data analysis, model tuning, data preparation, and reporting data insights to senior stakeholders.

Q: What qualifications are required for the Data Scientist position at Zilliant? You should have a Bachelor's or Master's Degree in Computer Science, Mathematics, Statistics, or a related STEM field (or equivalent working experience). Additionally, you should possess 3+ years' experience with SQL (T-SQL), Python, and Business Intelligence tools like Tableau or PowerBI. Familiarity with predictive modeling and various pricing strategies is also essential.

Q: What are the key skills and attributes Zilliant looks for in a Data Scientist? Zilliant seeks candidates who are self-starters, analytically minded, and avid learners. Clear communication skills are crucial, as you will need to explain technical and complex science in simple terms to non-technical senior stakeholders. The ability to adapt and manage changing requirements is also important.

Q: What benefits does Zilliant offer to its employees? Zilliant provides a comprehensive benefits package that includes medical, dental, and 401k plans with a company match. Employees also enjoy a generous Paid Time Off (PTO) policy to maintain a solid work/life balance, as well as comprehensive parental leave. The work schedule can be remote or hybrid, depending on team and personal preferences. Moreover, Zilliant has the financial and strategic backing of Madison Dearborn Partners (MDP).

Q: How can I prepare for an interview at Zilliant? To prepare for an interview at Zilliant, you should review your technical skills, especially in SQL, Python, and Business Intelligence tools. Practice explaining your past experiences and how they relate to the job description. Additionally, utilize resources like Interview Query to practice common interview questions and case studies specific to data science roles.

Looking to dive into a role that combines cutting-edge data science with transformative business impact? Zilliant offers a dynamic environment where your expertise can drive intelligent B2B commerce through advanced scientific models and pricing strategies. If this excites you, be sure to check out our comprehensive Zilliant Interview Guide on Interview Query. Our tailored resources cover various interview questions you might encounter and provide strategic guidance for acing your interview. Get ready to shine in your Data Scientist interview at Zilliant with the confidence and insights you'll gain from Interview Query.

Good luck with your interview!