Kickstarter is an innovative platform dedicated to bringing creative projects to life through crowdfunding. Known for its vibrant community of backers and creators, Kickstarter has revolutionized how new ideas are funded and executed.

As a Data Analyst at Kickstarter, you will play a crucial role in shaping the data-driven strategies that fuel the platform's growth. This position demands strong analytical skills and proficiency in data visualization, statistical analysis, and SQL. You will collaborate with cross-functional teams, providing insights that drive product development and community engagement.

If you aim to join an agile and forward-thinking team with a significant impact on the creative industry, this guide is for you. In this guide, we’ll walk you through the interview process, some commonly asked Kickstarter Data Analyst interview questions, and provide essential tips. Let's get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Kickstarter as a data analyst. Whether you were contacted by a Kickstarter 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 Kickstarter 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 Kickstarter data analyst 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 Kickstarter data analyst role usually is conducted through virtual means, including video conferences and screen sharing. Questions in this 1-hour long interview stage may revolve around Kickstarter’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 Kickstarter 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 Kickstarter.

Quick Tips For Kickstarter Data Analyst Interviews

• Understand Kickstarter’s Mission: Kickstarter focuses on creative projects. Be prepared to discuss how data can drive decision-making for creative success.
• Showcase Versatility: Kickstarter values multi-skilled individuals. Highlight your skills in various tools and techniques such as SQL, Python, and data visualization tools.
• Be Proactive and Analytical: Demonstrate your proactive approach towards problem-solving and analytical thinking related to crowdfunding and project success metrics.

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

• How would you set up an A/B test for button color and position changes? A team wants to A/B test multiple 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 set up this test?

• 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 anything suspicious about these results?

• Why might the average number of comments per user decrease despite user growth? A social media company launched in a new city and saw a slow decrease in the average number of comments per user from January to March, despite consistent user growth. What could be the reasons for this decrease, and what metrics would you investigate?

• What metrics would you use to determine the value of marketing channels? Given all the different marketing channels and their respective costs at a company selling B2B analytics dashboards, what metrics would you use to determine the value of each marketing channel?

• How would you find the 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?

• Write a function called find_bigrams to return a list of all bigrams in a sentence or paragraph. Write a function called find_bigrams that takes a sentence or paragraph of strings and returns a list of all its bigrams in order. A bigram is a pair of consecutive words.

• Write a query to get the last transaction for each day from a table of bank transactions. Given a table of bank transactions with columns id, transaction_value, and created_at, write a query to get the last transaction for each day. The output should include the id, datetime, and transaction amount, ordered by datetime.

• Write a function find_change to find the minimum number of coins for a given amount of change. Write a function find_change to find the minimum number of coins that make up the given amount of change cents. Assume we only have coins of value 1, 5, 10, and 25 cents.

• Write a function to simulate drawing balls from a jar based on their counts. 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.

• Write a function calculate_rmse to calculate the root mean squared error of a regression model. Write a function calculate_rmse to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

• What is the expected number of good ads rated by different types of raters?
• Suppose we have 100 raters each rating one ad independently. What's the expected number of good ads?
• Now suppose we have 1 rater rating 100 ads. What's the expected number of good ads?

• Suppose we have 1 ad, rated as bad. What's the probability the rater was lazy?

• Write a function to simulate coin tosses with a given probability of heads. Write a function that takes the number of tosses and the probability of heads as input and returns a list of randomly generated results ('H' for heads, 'T' for tails) equal in length to the number of tosses.

Example 1: Input: python tosses = 5 probability_of_heads = 0.6 Output: python coin_toss(tosses, probability_of_heads) -> ['H', 'T', 'H', 'H', 'T']

Example 2: Input: python tosses = 3 probability_of_heads = 0.2 Output: python coin_toss(tosses, probability_of_heads) -> ['T', 'T', 'T']

• Write a function to calculate the sample variance of a list of integers. Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places.

Example: Input: python test_list = [6, 7, 3, 9, 10, 15] Output: python get_variance(test_list) -> 13.89

• What is the probability of rolling at least one 3 with dice?
• What's the probability of rolling at least one 3 with 2 dice?

• What's the probability of rolling at least one 3 given (N) dice?

• What is the probability of finding an item on Amazon's website given its availability in warehouses? Amazon has a warehouse system where items on the website are located at different distribution centers across a city. In one example city, the probability that a specific item X is available at warehouse A or warehouse B are 0.6 and 0.8 respectively. Given that items are only found on the website if they exist in the distribution centers, what is the probability that the item X would be found on Amazon's website?

• What kind of model did the co-worker develop for loan approval? Your co-worker developed a model that takes customer inputs and returns if a loan should be given or not. What kind of model is this?

• How would you measure the difference between two credit risk models? Given that personal loans are monthly installments of payments, how would you measure the difference between two credit risk models within a timeframe?

• What metrics would you track to measure the success of a new credit risk model? What metrics would you track to measure the success of a new model predicting loan defaults?

• 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. What metrics would you use to track the accuracy and validity of the model?

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

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? Provide an example of the tradeoffs between the two.

• What happens when you run logistic regression on perfectly linearly separable data? You are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

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

Q: What does the Data Analyst position at Kickstarter entail?
The Data Analyst position at Kickstarter involves analyzing large sets of data to help the company make informed decisions. You will be expected to provide actionable insights from both qualitative and quantitative data, prepare comprehensive reports, and support various teams with data-related needs.

Q: What is the interview process like for the Data Analyst position at Kickstarter?
The interview process for a Data Analyst at Kickstarter typically includes an initial recruiter phone screen, a technical assessment, and one or more in-depth interviews with team members. These interviews will test your technical skills, analytical thinking, and cultural fit within the company.

Q: What skills are essential for a Data Analyst at Kickstarter?
To succeed as a Data Analyst at Kickstarter, you should possess strong analytical skills, proficiency in tools like SQL, Excel, and data visualization software such as Tableau or Looker. Familiarity with programming languages such as Python or R is also essential. Soft skills, including effective communication and problem-solving abilities, are crucial as well.

Q: What is the company culture like at Kickstarter?
Kickstarter prides itself on having a collaborative, innovative, and mission-driven culture. Employees are encouraged to be creative, share ideas freely, and work together to help creators bring their projects to life. The company values diversity, inclusion, and a strong sense of community.

Q: How should I prepare for the Data Analyst interview at Kickstarter?
To prepare for the Data Analyst interview at Kickstarter, start by researching the company and its mission. Practice common interview questions using resources like Interview Query, brush up on your technical skills, and be ready to discuss how your past experiences align with the role.

If you're aiming to become a Data Analyst at Kickstarter, your preparation is key to unlocking this opportunity. Understanding the company's core values, the role's requirements, and excelling in the interview process will set you apart. Dive deep into your preparation with our Kickstarter Interview Guide, where we cover a plethora of potential interview questions, strategic insights, and tips tailored for this position. At Interview Query, we arm you with the knowledge and confidence you need to ace your interviews. For comprehensive preparation across various roles and companies, check out our extensive collection of company interview guides. Should you have any questions, feel free to reach out. Wishing you the best of luck in your interview!