Clean Power Research is spearheading the energy transformation through cloud software that streamlines and values energy-related decisions for utilities, energy professionals, and consumers. As a burgeoning company, it serves top Fortune 500 utilities and leading renewable energy firms in the U.S. Clean Power Research aims to solve the energy industry's toughest challenges with innovative software, providing each employee with a vital role at the table.

As a Product Manager at Clean Power Research, you'll define and execute the vision for the SolarAnywhere® product line. This role involves cross-functional collaboration, feature development, and engaging with customers to understand industry challenges. Ideal candidates bring a technical background in PV modeling, and experience in SaaS product management, making impactful decisions to advance solar data and intelligence solutions.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Clean Power Research as a Product Manager. Whether you were contacted by a Clean Power Research 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 Clean Power Research 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 Clean Power Research Product Manager 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 Clean Power Research Product Manager role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Clean Power Research’s solar data systems, PV modeling, and software development.

In the case of product management roles, take-home assignments regarding product vision, strategy, and feature development may be incorporated. Apart from these, your proficiency against market sizing, pricing strategies, and technical expertise in data analysis and statistics may also be assessed during the round.

Depending on the seniority of the position, product 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 Clean Power Research office. Your strategic thinking, technical prowess, including software development, and PV 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 Product Manager role at Clean Power Research.

Quick Tips For Clean Power Research Product Manager Interviews

• Understand the Industry: Clean Power Research works in the cleantech and renewable energy space, predominantly focusing on solar. Make sure to familiarize yourself with the solar industry, its challenges, and trends.
• Highlight Technical Skills: Demonstrating your technical expertise, especially in SaaS, PV modeling, and data analysis, is critical. Review basics and advanced topics in these areas.
• Showcase Your Passion: Clean Power Research values employees who are passionate about cleantech and renewable energy. Use your past experiences and interests to reflect your enthusiasm for the industry.

Typically, interviews at Clean Power Research vary by role and team, but commonly Product Manager 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 merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

• Write a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Write a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. Write a function to search for a target value in the array. If the value is in the array, return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with the results?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test 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 design this test?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

• Why might the number of job applicants be decreasing while job postings remain the same? You observe that job postings per day have remained constant, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usability for analysis? Additionally, describe common issues in "messy" datasets.

• Is this a fair coin based on 10 flips resulting in 8 tails and 2 heads? You flipped a coin 10 times, resulting in 8 tails and 2 heads. Determine if this outcome suggests the coin is fair.

• How do you write a function to calculate sample variance for a list of integers? Write a function that calculates the sample variance for a given list of integers and rounds the result to 2 decimal places. For example, given test_list = [6, 7, 3, 9, 10, 15], the function should output 13.89.

• Is there anything suspicious about an A/B test with 20 variants where one is significant? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these findings.

• How do you find the median of a list where more than 50% of the elements are the same in O(1) time and space? Given a sorted list of integers where more than 50% of the list is the same repeating integer, write a function to return the median value in (O(1)) computational time and space. For example, given li = [1,2,2], the function should output 2.

• What are the drawbacks of the given student test score data layouts, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes to make the data more useful for analysis, and describe common problems seen in "messy" datasets.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice for this problem?

• How would you evaluate the performance of a decision tree model before and after deployment? If you decide to use a decision tree model, how would you assess its performance before deploying it and after it is in use?

• How does random forest generate the forest and why use it over logistic regression? Explain how a random forest algorithm generates its forest. Additionally, why might you choose random forest over logistic regression for certain problems?

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which scenarios would you prefer a bagging algorithm over a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? If 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?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to track the model's accuracy and validity?

If you want more insights about the company, check out our main Clean Power Research 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 Clean Power Research’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 Clean Power Research product manager 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!