Arrow Electronics, Inc. is a leading technology solutions provider, specializing in connecting customers with the latest innovative electronic components and enterprise computing solutions. Headquartered in Centennial, CO, Arrow Electronics operates on a global scale, delivering comprehensive services and support across various industries.

In the role of Data Scientist, you will join the Global Data Science team, where you'll be responsible for extracting valuable insights from data to inform business decisions. Your tasks will include solving complex business problems, developing machine learning models, and constructing data pipelines. You'll also collaborate closely with business users to ensure they understand and trust the models you develop. Experience with ETL development, HDFS, Power BI, and various data science techniques is essential. Arrow Electronics offers a competitive salary and robust benefits, including health insurance, 401k with matching contributions, and growth opportunities.

Prepare for your interview with Interview Query's comprehensive guide on Arrow Electronics.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Arrow Electronics as a Data Scientist. Whether you were contacted by an Arrow 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 Arrow 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 Arrow 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 Arrow 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 Arrow's data systems, ETL pipelines, machine learning models, 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 Arrow Electronics office. Your technical prowess, including programming, data modeling capabilities, and experience with Power BI, 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 Arrow Electronics.

Quick Tips for Arrow Electronics Data Scientist Interviews

• Plan to brush up on your technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Arrow Electronics interview include:

• Understand Business Impact: Arrow Electronics requires data scientists to interface with business users and make sense of developed business models. Be prepared to discuss how your technical solutions have previously driven business decisions.

• Technical Skills Proficiency: Brush up on your competence in SQL, Power BI, machine learning models, and ETL processes. Familiarize yourself with Big Data technologies or MPP technologies as well.

• Culture Fit: Understand Arrow's collaborative and innovative culture. Practice responding to behavioral questions with answers that reflect your ability to work in a team, manage projects, and drive growth opportunities.

Typically, interviews at Arrow Electronics 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 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.

• 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. Describe a strategy to find the mouse 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. Describe how you would decide which Dashers do these deliveries and whether the criteria for selection would 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. Identify factors that could have biased this result and what you would look into to verify the study's accuracy.

• 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. Describe how you would design this test and determine if the pricing increase is a good business decision.

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

• What is the probability of riders getting a coupon? A driver using the app picks up two passengers. Determine:

• The probability of both riders getting the coupon.

• The probability that only one of them will get the coupon.

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

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

• Is a coin fair if it comes up tails 8 times out of 10 flips? You flip a coin 10 times, resulting in 8 tails and 2 heads. Determine if the coin is fair.

• What are time series models and why are they needed? Describe what time series models are and explain why they are necessary when simpler regression models exist.

• How would you justify the complexity of building a neural network model and explain 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 the model and explain its predictions to non-technical stakeholders?

• 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 if a decision tree is the correct model? 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 random forest generates its forest. Additionally, why would you choose random forest over other algorithms like logistic regression?

• How would you explain linear regression to a child, a first-year college student, and a seasoned 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.

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

What is the average salary for a Data Scientist at Arrow Electronics?

Q: What does a Data Scientist at Arrow Electronics do? A: A Data Scientist at Arrow Electronics works in the Global Data Science team to uncover insights from data that assist in making business decisions. This includes solving business problems, building data pipelines, developing machine learning models, performing ETL development, and creating reports in Power BI. The role also involves collaborating with business leaders to push prototypes to production and maintaining documentation and workflows.

Q: What are the minimum requirements for the Data Scientist position at Arrow Electronics? A: The position requires a bachelor’s degree in Business Analytics, Data Science, or related fields and 7 years of relevant experience, or a master’s degree with 4 years of experience, or a PhD with 1 year of experience. Experience in SQL, Power BI, ETL pipelines, big data technologies, and programming languages like Python and SQL is also necessary.

Q: What is the salary range for the Data Scientist role at Arrow Electronics? A: The salary range for the Data Scientist position at Arrow Electronics is between $136,885 and $150,574 per year.

Q: What benefits does Arrow Electronics offer to its employees? A: Arrow Electronics provides a competitive compensation package, including medical, dental, and vision insurance, a 401(k) plan with matching contributions, paid time off, tuition reimbursement, HSA/HRA options, growth opportunities, and short-term/long-term disability insurance.

Q: Where is the Arrow Electronics Data Scientist position based? A: The position is based in Centennial, CO, USA.

If you're aiming to make a significant impact as a Data Scientist at Arrow Electronics, now is the time to take action! Dive into an environment where your analytical insights and machine learning skills can drive crucial business decisions and discover innovative trends. With attractive financial rewards, comprehensive benefits, and ample opportunities for professional growth, Arrow Electronics is the perfect place to advance your career.

If you want more insights about the company, check out our main Arrow Electronics Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, where you can learn more about Arrow Electronics’ 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 Arrow Electronics 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!