Hewlett Packard Enterprise (HPE) is a global tech leader known for driving innovation through data analytics, machine learning, and cloud computing. If you’re passionate about leveraging data to solve complex business challenges, HPE’s Data Scientist positions offer a unique opportunity to work in a dynamic, agile environment.

In this role, you’ll partner with cross-functional teams to harness data from one of the largest data lakes in the world, driving AI/ML solutions to improve customer experiences and strategic business outcomes. From developing predictive models and conducting A/B tests, to identifying AI opportunities and optimizing supply chains, HPE Data Scientists are at the forefront of technological advancement.

If you're looking to advance your career and contribute to pioneering data-driven solutions, this guide from Interview Query is designed to help you navigate the interview process for HPE’s Data Scientist positions. 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 Hewlett Packard Enterprise (HPE) as a Data Scientist. Whether you were contacted by HPE's recruitment team or have taken the initiative yourself, carefully review the job description and tailor your CV and cover letter according to the prerequisites. Highlight relevant skills and mention your past work experiences to stand out.

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted applications, a recruiter from HPE will contact you to verify details like your experiences and skill level. The call will include behavioral questions, and in some cases, the HPE Data Scientist hiring manager might be present to answer your questions and engage in surface-level technical and behavioral discussions. The recruiter call should take about 30 minutes.

Technical Virtual Interview

Navigating successfully through the recruiter round will land you an invitation for the technical screening round. This round is typically conducted virtually, involving video conferences and screen-sharing tools.

Questions in this 1-hour long interview will likely focus on topics such as: - Linear Regression - Logistic Regression - Neural Networks - Probability and Statistics - Linear Algebra - Regression Analysis

The interview may also assess your proficiency in SQL, Python, and machine learning fundamentals.

Depending on the seniority of the position, you might also be given a take-home assignment related to data modeling, predictive analytics, or data visualization.

Onsite Interview Rounds

After passing the technical screening, you'll be invited to the onsite interview loop, either virtually or at an HPE location. These multiple rounds will further test your technical skills and include problem-solving tasks related to real-world scenarios. You might have to showcase any take-home assignments or case studies you'll be given.

Quick Tips For HPE Data Scientist Interviews

Preparing adequately can significantly improve your chances. Here are a few tips:

1. Solidify Your Basics: Brush up on foundational concepts like linear regression, logistic regression, neural networks, and probability. Ensure your understanding of linear algebra and regression analyses is strong.

2. Show Passion for Data Science: HPE values innovation driven by data, so emphasize your passion for data science and your experience in using data to drive business outcomes.

3. Demonstrate Problem-Solving Skills: Be prepared to discuss how you’ve tackled past challenges in your role as a data scientist. Highlight your analytical approach and how you balance technical constraints with business needs.

For more comprehensive preparation, consider signing up at Interview Query for exclusive resources and practice questions tailored to HPE’s interview process.

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

• How much should we budget for the coupon initiative in total? A ride-sharing app has a probability (p) of dispensing a $5 coupon to a rider. The app services (N) riders. Calculate the total budget needed for the coupon initiative.

• What is the probability of both riders getting the coupon? A driver using the app picks up two passengers. Determine the probability that both riders will receive the coupon.

• What is the probability that only one of them will get the coupon? A driver using the app picks up two passengers. Determine the probability that only one of the riders will receive the coupon.

• What is a confidence interval for a statistic? Explain what a confidence interval is, why it is useful, and how to calculate it.

• What is the probability that item X would be found on Amazon's website? Amazon has a warehouse system where items are located at different distribution centers. Given the probabilities that item X is available at warehouse A (0.6) and warehouse B (0.8), calculate the probability that item X would be found on Amazon's website.

• 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.

• What are time series models and why do we need them? Describe what time series models are and explain why they are necessary compared to less complicated regression models.

• 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 find the maximum number in a list of integers. Given a list of integers, write a function that returns the maximum number in the list. If the list is empty, return None.

• Create a function convert_to_bst to convert a sorted list into a balanced binary tree. Given a sorted list, create a function convert_to_bst that converts the list into a balanced binary tree. The output binary tree should be balanced, meaning the height difference between the left and right subtree of all the nodes should be at most one.

• Write a function to simulate drawing balls from a jar. Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

• Develop a function can_shift to determine if one string can be shifted to become another. Given two strings A and B, write a function can_shift to return whether or not A can be shifted some number of places to get B.

• How would you explain linear regression to a child, a college student, and a mathematician? Explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician. Tailor your explanations to each audience's understanding level.

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? As a data scientist at a bank, you need to build a decision tree model to predict if a borrower will repay a personal loan. Evaluate whether a decision tree is the correct model and how you would assess its performance before and after deployment.

• 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. Justify the complexity of the model and explain its predictions to non-technical stakeholders.

• How does random forest generate the forest, and why use it over logistic regression? Explain how random forest generates its forest and discuss why it might be preferred over other algorithms like logistic regression.

• What are the key differences between classification models and regression models? Describe the main differences between classification models and regression models.

• What are the drawbacks of having student test scores organized in the given layouts? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts and suggest formatting changes to make the data more useful for analysis. Additionally, describe common problems seen in "messy" datasets.

• How would you locate a mouse in a 4x4 grid using the fewest scans? You have a 4x4 grid with a mouse trapped in one of the cells. You can scan subsets of cells to know if the mouse is within that subset. How would you determine the mouse's location using the fewest number of scans?

• How would you select Dashers for Doordash deliveries in NYC and Charlotte? Doordash is launching delivery services in New York City and Charlotte and needs a process for selecting dashers. How would you decide which Dashers do these deliveries, and would the criteria for selection be the same for both cities?

• What factors could bias Jetco's study on boarding times? Jetco, a new airline, had a study showing it has the fastest average boarding times. What factors could have biased this result, and what would you investigate?

• How would you design an A/B test to evaluate a pricing increase for a B2B SAAS company? You work at a B2B SAAS company interested in testing different subscription pricing levels. Your project manager asks you to run a two-week-long A/B test to test an increase in pricing. How would you design this test and determine if the pricing increase is a good business decision?

What is the average salary for a Data Scientist at Hewlett Packard Enterprise?

Q: What can I expect from the interview process for a Data Scientist position at Hewlett Packard Enterprise? The interview process at Hewlett Packard Enterprise typically involves multiple stages, including an initial online assessment (OA), followed by technical interviews. These interviews often cover fundamental concepts such as linear regression, logistic regression, neural networks, probability, and statistics. You may also be asked to elaborate on your past internship and relevant experiences.

Q: What kind of projects will I work on as a Data Scientist at Hewlett Packard Enterprise? As a Data Scientist at Hewlett Packard Enterprise, you will have the opportunity to work on a variety of projects, including developing innovative AI/ML models, performing A/B testing and experimentation, identifying AI opportunities, and deploying models across millions of devices. You will collaborate closely with product managers, data scientists, and software engineers to deliver on the data science roadmap in a fast-paced, agile environment.

Q: What qualifications do I need for the Data Scientist position? Candidates typically need a Master’s degree or PhD in a highly quantitative field such as Computer Science, Machine Learning, Statistics, or Physics. At least 10 years of industry experience in predictive modeling and data science roles is generally required, along with proficiency in programming languages such as Python and SQL. Strong analytical and problem-solving skills, experience with machine learning frameworks, and the ability to work collaboratively in a high-paced environment are also crucial.

Q: What skills are crucial for this role? To be successful in the Data Scientist role, you need extensive experience with statistics, algorithms, and data management. Expert knowledge of SQL, Python, and PySpark, along with a deep understanding of current machine learning concepts and tools, are critical. Additionally, excellent interpersonal and project management skills, the ability to create insightful data visualizations, and the capability to communicate effectively with non-technical stakeholders are highly valued.

Q: How can I prepare for the interview? Preparing for an interview at Hewlett Packard Enterprise involves refreshing your knowledge of fundamental data science concepts and practicing coding problems. Utilizing resources from Interview Query can help you tackle common interview questions and case studies. Make sure to review your past experiences, particularly focusing on areas relevant to the job description, such as machine learning, data analytics, and your programming skills.

If you want more insights about the company, check out our main HP 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 HP’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 HP Data Scientist 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!