Enverus is a pioneering company dedicated to providing state-of-the-art data analytics and decision-making solutions to the energy industry. Known for its commitment to innovation and cutting-edge technology, Enverus helps clients navigate the complexities of the energy market.

As a Data Scientist at Enverus, you'll blend your technical expertise with the company's deep industry knowledge. You'll be responsible for developing predictive models, analyzing vast datasets, and generating actionable insights that drive business decisions. This role demands proficiency in machine learning, statistical analysis, and algorithm development.

If you're eager to contribute to the transformation of the energy sector with Enverus, this guide is for you. We'll cover the interview process, commonly asked questions, and useful tips to help you prepare. 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 Enverus as a Data Scientist. Whether you were contacted by an Enverus 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 Enverus 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 Enverus 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 Enverus 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 Enverus’ 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 Enverus 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 Enverus.

Quick Tips For Enverus Data Scientist Interviews

• Should contain three tips for interviewing for this specific company based on interview experiences.

Example:

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 Enverus interview include:

• Know Your Enverus Products: Enverus questions are standardized and rely heavily on situational scenarios with their products. Study Enverus’ large breadth of products and understand how you would personally improve or test them.
• Be Data Driven: Enverus’ data science interviews assess how well you can provide business-driving insights with data science. Brush up on your knowledge of statistics and probability, given these questions can be some of the hardest to solve.
• Embody the Spirit: Enverus at its core has a collaborative, employee-focused culture that values innovation. Practice responding to behavioral questions with answers that touch on Enverus’ core values.

Typically, interviews at Enverus 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. Given a rotated sorted array and a target value, write a function to search for the target value. If the value is in the array, return its index; otherwise, return -1. Bonus: 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 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 found 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. Example: Input: test_list = [6, 7, 3, 9, 10, 15] 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 where over 50% of elements are the same? 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 and formatting changes needed for messy datasets? You have data on student test scores in two different layouts. Identify the drawbacks of the current organization, suggest formatting changes for better analysis, and describe common problems in messy datasets. Example 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 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? You are comparing 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 Scientist at Enverus?

Q: What is the interview process like at Enverus for a Data Scientist position? A: The interview process at Enverus typically includes an initial phone screen with a recruiter, followed by technical interviews and sometimes a case study or project. These stages are designed to assess your analytical skills, technical expertise, and cultural fit with the company.

Q: What skills are essential for a Data Scientist role at Enverus? A: To succeed as a Data Scientist at Enverus, you should have strong technical skills in statistics, machine learning, and programming (especially Python and R). Experience with data visualization tools, SQL, and knowledge of the energy industry can also be highly beneficial.

Q: What does the company culture at Enverus look like? A: Enverus values innovation, collaboration, and continuous learning. The company encourages employees to share their insights and contribute to projects that drive the energy sector forward. It’s a fast-paced and dynamic environment that rewards creativity and hard work.

Q: What types of projects might a Data Scientist work on at Enverus? A: As a Data Scientist at Enverus, you could be involved in projects related to energy market analysis, predictive modeling, and optimization algorithms. Your work will likely focus on providing data-driven insights to help clients make informed decisions in the energy sector.

Q: How can I prepare for an Enverus Data Scientist interview? A: To prepare for an Enverus Data Scientist interview, brush up on your technical skills, particularly in machine learning, statistics, and data analysis. Practice common interview questions on platforms like Interview Query, review your past projects, and be ready to discuss how your experience aligns with the company's needs.

If you want more insights about the company, check out our main Enverus 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 Enverus’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 Enverus machine learning engineer 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!