Draper is a pioneering research and development company specializing in advanced technology solutions. Known for its innovative work across various sectors, Draper creates next-generation capabilities to solve complex challenges in defense, space, biomedical, and transportation industries.

Joining Draper as a Data Analyst entails rigorous analytical tasks, where you’ll be leveraging your technical skills to extract insights from vast datasets, support decision-making processes, and contribute to mission-critical projects. The role demands proficiency in data analysis, programming, statistical methods, and presentation of complex information in an understandable format.

If you’re considering Draper for your next career move, this guide on Interview Query is tailored for you. Here, we’ll take you through the interview process, common interview questions for a Data Analyst position at Draper, and share some valuable tips to help you prepare effectively. 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 Draper as a data analyst. Whether you were contacted by a Draper 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 Draper 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 Draper data analyst hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also engage 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 Draper 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 Draper’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 Draper 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 analyst role at Draper.

Quick Tips For Draper Data Analyst Interviews

• Know Your Draper Projects: Understand the various projects Draper is involved in. Be prepared to discuss how your skills can directly contribute to these projects.
• Be Methodical: Draper values a methodical approach to problem-solving. Brush up on your data analysis techniques and ensure you can walk interviewers through your process clearly.
• Cultural Fit: Draper places a high value on cultural fit. Reflect on how your previous experiences and personal values align with Draper's mission and culture.

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

• Identify all duplicate values in a list of integers. Given a list of integers, identify all the duplicate values in the list. Assume that the list can contain both positive and negative numbers, and the order of the list does not matter. A number is considered a duplicate if it appears more than once in the list. Return a list of the duplicate numbers.

Example 1:

Input: python nums = [1, 2, 3, 1, 2, 3]

Output: python find_duplicates(nums) -> [1, 2, 3]

The numbers 1, 2, and 3 all appear more than once in the list, so they are considered duplicates.

Example 2:

Input: python nums = [1, -1, 2, 3, 3, -1] Output: python find_duplicates(nums) -> [-1, 3] The numbers -1 and 3 both appear more than once in the list, so they are considered duplicates. Note that the order of the output does not matter.

Example 3:

Input: python nums = [1, 2, 3, 4, 5] Output: python find_duplicates(nums) -> [] None of the numbers in the list appear more than once, so there are no duplicates.

• Select the top five most expensive projects by budget to employee count ratio. Write a query to account for duplicate rows in the employee_projects table and select the top five most expensive projects by budget to employee count ratio.

Example:

Input:

projects table

| column | type | |------------|----------| | id | INTEGER | | title | VARCHAR | | state_date | DATETIME | | end_date | DATETIME | | budget | INTEGER |

employee_projects table

| Column | Type | |--------|--------| | project_id | INTEGER | | employee_id | INTEGER |

Output:

| Column | Type | | --- | --- | | title | VARCHAR | | budget_per_employee | FLOAT |

• Create a subquery or common table expression to find the top 3 ads and perform various joins. Create a subquery or common table expression named top_ads containing the top 3 ads by popularity and return the number of rows resulting from different join operations with the ads table.

Example:

Input:

|Column|Type| |---|---| |id|INTEGER| |name|VARCHAR|

Output:

|Column|Type| |---|---| |join_type|VARCHAR| |number_of_rows|INTEGER|

• Find employees who joined before their manager. Given two tables, employees and managers, find the names of all employees who joined before their manager.

Example:

Input:

employees table

| Column | Type | |------------|----------| | id | INTEGER | | first_name | VARCHAR | | last_name | VARCHAR | | manager_id | INTEGER | | join_date | DATETIME |

managers table

| Column | Type | |------------|----------| | id | INTEGER | | name | VARCHAR | | join_date | DATETIME |

Output:

| Column | Type | |------------|----------| | employee_name | VARCHAR |

• Write a function to rotate a matrix by 90 degrees clockwise. Given an array filled with random values, write a function rotate_matrix to rotate the array by 90 degrees in the clockwise direction.

Example:

Input: ```python import numpy as np

array = np.array( [[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8], [5, 6, 7, 8, 9]] ) ```

array

Output:

• How do we select the best 10,000 customers for a pre-launch of a new show? You are working as a data scientist at Amazon Prime Video, and they want to test the launch of a new show on 10,000 customers first. How would you go about selecting the best 10,000 customers for the pre-launch?

• What is the process for pre-launching a TV show on Amazon Prime to measure performance? Describe the steps involved in pre-launching a TV show on Amazon Prime to measure its performance.

• How would you evaluate the success of an A/B test on free shipping increasing conversions? You work at an eCommerce startup and ran an A/B test on the checkout product page to see if surfacing free shipping increases conversions. The control group had no specification of free shipping, while the experiment group did. How would you evaluate the results and determine if the test was successful?

• How would you conduct an experiment to test displaying ETA as a range instead of a direct estimate? You work at Uber, and a PM suggests displaying ETA as a range (e.g., 3-7 minutes) instead of a direct estimate (e.g., 5 minutes). How would you conduct this experiment and determine if the results are significant?

• How would you decide whether Google should build a game feature for Google Home? You are tasked with pitching a new feature for Google Home, and a co-worker suggests building a game feature. How would you go about deciding whether Google should build it?

• How would you measure the effectiveness of extra pay for delivery drivers during peak hours? You work at a food delivery company and need to measure the effectiveness of giving extra pay to delivery drivers during peak hours to meet consumer demand. How would you measure this?

• What is the difference between Logistic and Linear Regression? When would you use one instead of the other? Explain the key differences between Logistic and Linear Regression. Describe scenarios where each method is more appropriate to use in practice.

• How would you explain what a p-value is to someone who is not technical? Explain the concept of a p-value in simple terms to someone without a technical background. Use analogies or everyday examples to make it understandable.

• What is the difference between Logistic and Linear Regression? When would you use one instead of the other in practice? Describe the key differences between Logistic and Linear Regression. Provide practical scenarios where each type of regression would be appropriately used.

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

Q: What is the interview process at Draper like?

A: Draper’s interview process typically involves an initial phone screen with a recruiter, followed by one or more technical interviews, and finally, onsite or virtual interviews. These stages are designed to assess your technical expertise, problem-solving skills, and cultural fit.

Q: What skills are required to work as a Data Analyst at Draper?

A: To work as a Data Analyst at Draper, you should have strong analytical and problem-solving skills, proficiency in data manipulation tools like SQL and Excel, and experience with data visualization tools such as Tableau or PowerBI. Knowledge of programming languages like Python or R is also beneficial.

Q: What types of projects do Data Analysts at Draper work on?

A: Data Analysts at Draper work on a variety of projects that involve analyzing complex datasets to derive actionable insights. These projects can range from improving internal processes to supporting decision-making with data-driven strategies, and even contributing to product development.

Q: How can I prepare for an interview at Draper?

A: To prepare for an interview at Draper, research the company's mission and projects, practice common data analyst interview questions, and review your technical skills. You can also use Interview Query to practice and refine your problem-solving and analytical skills.

Q: What is the company culture like at Draper?

A: Draper has a collaborative and innovative company culture that emphasizes continuous learning and development. The company values diverse perspectives and encourages employees to bring their unique backgrounds and ideas to the table.

For those aiming to join Draper as a Data Analyst, thorough preparation is key. We've compiled a detailed Draper Interview Guide to provide you with insights into the company's interview process and help you navigate potential questions. Our platform, Interview Query, offers specialized guides for various roles including data analyst and other positions to deepen your understanding of Draper’s hiring criteria.

At Interview Query, we equip you with the strategies, confidence, and knowledge you need to tackle every challenge in your Draper interview. Explore all our company interview guides to maximize your preparation. Feel free to reach out with any questions.

Good luck with your interview!