The Renorun Data Analyst interview guide, interview questions, salary data, and interview experiences.

Renorun Data Analyst Interview Guide

Getting ready for an Data Analyst interview at Renorun? The Renorun Data Analyst interview span across 10 to 12 different question topics. In preparing for the interview:
<ul>
<li>Know what skills are necessary for Renorun Data Analyst roles.</li>
<li>Gain insights into the Data Analyst interview process at Renorun.</li>
<li>Practice real Renorun Data Analyst interview questions.</li>
</ul>
Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Renorun Data Analyst interview.


Getting ready for an Data Analyst interview at Renorun? The Renorun Data Analyst interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for Renorun Data Analyst roles.
- Gain insights into the Data Analyst interview process at Renorun.
- Practice real Renorun Data Analyst interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Renorun Data Analyst interview.
        

Overview

Renorun Data Analyst Salary

We've gathered this data from parsing thousands of interview experiences sourced from members.

We've gathered this data from parsing thousands of interview experiences sourced from members.

Renorun Data Analyst Interview Process

As part of the financial management in a large corporation, the CFO wants to review the expenses in all departments for the previous financial year (2022).

Write a SQL query to calculate the total expenditure for each department in 2022. Additionally, for comparison purposes, return the average expense across all departments in 2022.

Note: The output should include the department name, the total expense, and the average expense (rounded to 2 decimal places). The data should be sorted in descending order by total expenditure.

<h3>Example:</h3>

Input:

<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>name</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<code>expenses</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>department_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>amount</td>
<td>FLOAT</td>
</tr>

<tr>
<td>date</td>
<td>DATE</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>department_name</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>total_expense</td>
<td>FLOAT</td>
</tr>

<tr>
<td>average_expense</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>

Calculate total and average expenses for each department.

Department Expenses

The probability that it will rain tomorrow is dependent on whether or not it is raining today and whether or not it rained yesterday.

If it rained yesterday and today, there is a 20% chance it will rain tomorrow. If it rained one of the days, there is a 60% chance it will rain tomorrow. If it rained neither today nor yesterday, there is a 20% chance it will rain tomorrow.

Given that it is raining today and that it rained yesterday, write a function <code>rain_days</code> to calculate the probability that it will rain on the nth day after today.

Example:

Input:

<pre tabindex="0" class="chroma"><code>n=5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def rain_days(n) -&gt; 0.39968
</code></pre>

Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today.

Rain in N Days

You’re given two dataframes. One contains information about addresses and the other contains relationships between various cities and states:

Example:

<code>df_addresses</code>

<table>
<thead>
<tr>
<th>address</th>
</tr>
</thead>

<tbody>
<tr>
<td>4860 Sunset Boulevard, San Francisco, 94105</td>
</tr>

<tr>
<td>3055 Paradise Lane, Salt Lake City, 84103</td>
</tr>

<tr>
<td>682 Main Street, Detroit, 48204</td>
</tr>

<tr>
<td>9001 Cascade Road, Kansas City, 64102</td>
</tr>

<tr>
<td>5853 Leon Street, Tampa, 33605</td>
</tr>
</tbody>
</table>
<code>df_cities</code>

<table>
<thead>
<tr>
<th>city</th>
<th>state</th>
</tr>
</thead>

<tbody>
<tr>
<td>Salt Lake City</td>
<td>Utah</td>
</tr>

<tr>
<td>Kansas City</td>
<td>Missouri</td>
</tr>

<tr>
<td>Detroit</td>
<td>Michigan</td>
</tr>

<tr>
<td>Tampa</td>
<td>Florida</td>
</tr>

<tr>
<td>San Francisco</td>
<td>California</td>
</tr>
</tbody>
</table>
Write a function <code>complete_address</code> to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Input:

<pre tabindex="0" class="chroma"><code>import pandas as pd

addresses = {&#34;address&#34;: [&#34;4860 Sunset Boulevard, San Francisco, 94105&#34;, &#34;3055 Paradise Lane, Salt Lake City, 84103&#34;, &#34;682 Main Street, Detroit, 48204&#34;, &#34;9001 Cascade Road, Kansas City, 64102&#34;, &#34;5853 Leon Street, Tampa, 33605&#34;]}

cities = {&#34;city&#34;: [&#34;Salt Lake City&#34;, &#34;Kansas City&#34;, &#34;Detroit&#34;, &#34;Tampa&#34;, &#34;San Francisco&#34;], &#34;state&#34;: [&#34;Utah&#34;, &#34;Missouri&#34;, &#34;Michigan&#34;, &#34;Florida&#34;, &#34;California&#34;]}

df_addresses = pd.DataFrame(addresses)
df_cities = pd.DataFrame(cities)
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def complete_address(df_addresses,df_cities) -&gt;
</code></pre>

<table>
<thead>
<tr>
<th>address</th>
</tr>
</thead>

<tbody>
<tr>
<td>4860 Sunset Boulevard, San Francisco, California, 94105</td>
</tr>

<tr>
<td>3055 Paradise Lane, Salt Lake City, Utah, 84103</td>
</tr>

<tr>
<td>682 Main Street, Detroit, Michigan, 48204</td>
</tr>

<tr>
<td>9001 Cascade Road, Kansas City, Missouri, 64102</td>
</tr>

<tr>
<td>5853 Leon Street, Tampa, Florida, 33605</td>
</tr>
</tbody>
</table>

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

Renorun Data Analyst Interview Questions

<h3>Job Description</h3><div>Job Description<div>Captions is the leading video AI company, building the future of video creation. Over 10 million creators and businesses have used Captions to create videos for social media, marketing, sales, and more. We're on a mission to serve the next billion. We are a rapidly growing team of ambitious, experienced, and devoted engineers, researchers, designers, marketers, and operators based in NYC. You'll join an early team and have an outsized impact on the product and the company's culture.We’re very fortunate to have some the best investors and entrepreneurs backing us, including Index Ventures (Series C lead), Kleiner Perkins (Series B lead), Sequoia Capital (Series A and Seed co-lead), Andreessen Horowitz (Series A and Seed co-lead), Uncommon Projects, Kevin Systrom, Mike Krieger, Lenny Rachitsky, Antoine Martin, Julie Zhuo, Ben Rubin, Jaren Glover, SVAngel, 20VC, Ludlow Ventures, Chapter One, and more. Check out our latest financing milestone and some other coverage: The Information: 50 Most Promising StartupsFast Company: Next Big Things in TechThe New York Times: When A.I. Bridged a Language Gap, They Fell in LoveBusiness Insider: 34 most promising AI startups** Please note that all of our roles will require you to be in-person at our NYC HQ (located in Union Square) ** About the Role:Captions is looking for a Business Intelligence Data Analyst to drive holistic, data-driven decision-making across our organization. In this role, you’ll be instrumental in building out business intelligence at Captions, and in shaping how we use data to inform strategy, optimize operations, and fuel growth. You will partner cross-functionally with teams including Operations, Finance, Marketing, Data Science, Engineering, and Leadership to provide insights that translate into action and measurable business impact. This is an ideal opportunity for a highly analytical, strategic thinker who enjoys leveraging data to solve complex business problems in a fast-paced and collaborative environment. Key Responsibilities:<ul><li>As the first business data analyst at Captions, establish and optimize company-wide analytics, reporting systems, and processes to ensure clear visibility into performance across all business functions.</li><li>Collaborate cross-functionally with teams (Operations, Marketing, Finance, Product) to create (build, maintain, and improve) dashboards and reports that provide actionable insights.</li></ul><ul><li>Work closely with our Head of Finance to build out our financial data capabilities and support ongoing financial analysis. </li><li>Develop, maintain, and optimize SQL queries to extract and manipulate data from multiple databases for reporting and analytics purposes.</li><li>Analyze and interpret complex data sets to generate insights on key business metrics.</li><li>Use data to identify trends, opportunities, and areas for improvement in customer acquisition, retention, and revenue growth.</li><li>Present findings and insights to stakeholders through clear data visualization and reporting.</li><li>Support data-driven decision-making by providing ad-hoc analysis and reporting on business performance.</li><li>Collaborate with data science and engineering teams to support more advanced analytics and predictive modeling efforts.</li></ul>Requirements:<ul><li>A BS/MS in Mathematics, Statistics, Computer Science, or a related field.</li><li>5+ years of experience leveraging analytics to drive key business decisions across business intelligence, finance, strategy, and/or marketing.</li><li>Proficiency in data manipulation, i.e., SQL and Python.</li><li>Proven ability to build data visualizations that inform business decisions using tools such as Sigma, Tableau, Power BI, Looker, or similar platforms.</li><li>Strong understanding of critical business metrics in a subscription-based software business (e.g., churn, LTV, CAC, MRR, ARR, NRR).</li><li>Demonstrated strategic problem-solving skills, capable of breaking down ambiguous problems and applying a first principles approach to develop solutions.</li><li>Strong product and business intuition, with the ability to contextualize insights within broader business goals and an understanding of key metrics.</li><li>Excellent verbal, presentation, and written communication skills, with the ability to convey complex data in a clear and concise manner to various audiences.</li><li>Resourceful and scrappy; you find innovative ways to achieve goals and overcome challenges.</li></ul>Bonus Points: <ul><li>Proficiency with Python for data analysis or exposure to data science methods.</li></ul><ul><li>Capable of building data pipelining solutions.</li></ul>Benefits:<ul><li>Comprehensive medical, dental, and vision plans</li><li>401K with employer match</li><li>Commuter Benefits</li><li>Catered lunch multiple days per week</li><li>Dinner stipend every night if you're working late and want a bite! </li><li>Doordash DashPass subscription</li><li>Health & Wellness Perks (Talkspace, Kindbody, One Medical subscription, HealthAdvocate, Teladoc)</li><li>Multiple team offsites per year with team events every month</li><li>Generous PTO policy and flexible WFH days </li></ul>Captions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.Please note benefits apply to full time employees only.Compensation Range: $130K - $160K</div></div>

Renorun Data Analyst Interview Guide

Overview

Renorun Data Analyst Salary

Renorun Data Analyst Interview Process

Renorun Data Analyst Interview Questions

Renorun Data Analyst Jobs