Bolt is an innovative and dynamic company making waves in the tech industry with its cutting-edge solutions in transportation and online payments. Known for its rapid growth and forward-thinking approach, Bolt is an exciting place to work.

The Data Analyst position at Bolt is pivotal for driving data-driven decisions within the company. This role involves aggregating data insights, interpreting complex data sets, and supporting strategic initiatives to enhance Bolt’s market presence. Candidates are expected to possess strong analytical skills, proficiency in data tools, and a keen eye for detail.

If you are preparing for an interview with Bolt, this guide will walk you through the interview process, sample questions, and essential tips to help you succeed. Your journey to becoming a Data Analyst at Bolt starts here!

Submitting Your Application

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

Quick Tips For Bolt Data Analyst Interviews

• Prepare for Scheduling Issues: Unfortunately, some candidates have experienced scheduling inconsistencies, including no-shows. Confirm your interview time via email and follow up if you do not receive a response.
• Emphasize Technical Skills: While it's important to discuss your experiences, ensure you convey your technical aptitude, especially regarding SQL, ETL processes, and data visualization.
• Showcase Analytical Thinking: Bolt values candidates who can translate data into business insights. Be ready to discuss examples of how you've done this in previous roles.

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

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

• What are the drawbacks of the given student test score datasets, and how would you reformat them? Analyze the provided student test score datasets for potential issues. Suggest formatting changes to make the data more useful for analysis. Also, describe common problems in "messy" datasets.

• What metrics would you use to determine the value of each marketing channel? Given the marketing costs for different channels at a B2B analytics company, identify the metrics you would use to evaluate the value of each marketing channel.

• How would you determine the next partner card using customer spending data? Using customer spending data, outline the process to identify the most suitable partner for a new partner card, similar to Starbucks or Whole Foods chase credit cards.

• How would you investigate if the redesigned email campaign led to the increase in conversion rates? Given the fluctuating conversion rates before and after a new email campaign, describe how you would determine if the redesigned email journey caused the increase in conversion rates or if other factors were involved.

• How would you explain what a p-value is to someone who is not technical?

• How does random forest generate the forest and why use it over logistic regression? Explain the process of how random forest generates multiple decision trees to form a forest. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms and describe scenarios where bagging is preferred over boosting. Provide examples of the tradeoffs between the two, such as variance reduction in bagging and bias reduction in boosting.

• How would you evaluate and compare two credit risk models for personal loans?

• Identify the type of model developed by the co-worker for loan approval.
• Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installment payments.

• List the metrics to track for measuring the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

• What’s the difference between Lasso and Ridge Regression? Explain the key differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

• Write a function search_list to check if a target value is in a linked list. Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with keys value and next. If the linked list is empty, you'll receive None.

• Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product. Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

• Create a function digit_accumulator to sum every digit in a string representing a floating-point number. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

• Develop a function to parse the most frequent words used in poems. You're hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

• Write a function rectangle_overlap to determine if two rectangles overlap. You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

If you want more insights about the company, check out our main Bolt 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 Bolt’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 Bolt data analyst 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!