Clean Power Research® is at the forefront of the energy revolution, leveraging cloud software to enhance energy-related decisions and processes for utilities, energy professionals, and consumers. Our growing company is trusted by top Fortune 500 utilities and leading renewable energy firms in the U.S. We continually advance our market reach and impact through innovative software solutions that address the industry's most challenging problems.

As a Software Engineer with Clean Power Research, you'll develop SaaS solutions in a cloud environment using cutting-edge tools. You'll tackle exciting challenges around machine learning and scientific algorithms, particularly with our SolarAnywhere® team, which predicts solar energy output from real-time satellite imagery. This full-stack role involves end-to-end feature ownership using technologies like C#, JavaScript, and SQL. You'll be part of an agile team, directly engaging with customers and influencing product, design, and technology decisions.

Explore the vital renewable energy sector and prepare for your interview with Interview Query!

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 Software Engineer. 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 Software Engineer 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 Software Engineer 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 cloud infrastructure, software development practices, and coding challenges in C#, JavaScript, JS frameworks, and SQL in a cloud-hosted (AWS) environment.

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 Clean Power Research office. Your technical prowess, including programming, problem-solving abilities, and understanding of scientific algorithms related to solar prediction, 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 Software Engineer role at Clean Power Research.

Quick Tips For Your Clean Power Research Software Engineer Interviews

You should plan to brush up on any technical skills and try as many practice interview questions from Interview Query and mock interviews as possible. A few tips for acing your Clean Power Research interview include:

• Understand Renewable Energy Concepts: Clean Power Research focuses on renewable energy solutions. Demonstrating a strong understanding of the renewable energy market and the company's specific technologies (e.g., SolarAnywhere) can be crucial.

• Brush Up on Cloud Infrastructure Knowledge: Proficiency with cloud environments, especially AWS, is vital. Familiarize yourself with cloud-hosted architectures, data handling, and scalability issues.

• Showcase Problem-Solving Skills: Expect questions around scientific algorithms and machine learning, often used in Clean Power Research's products. Be prepared to discuss examples where you have implemented these in your previous work.

Typically, interviews at Clean Power Research vary by role and team, but commonly Software Engineer 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: What's the time complexity?

• Create 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. Note: Complexity of (O(n)) required.

• Develop 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. You are given a target value to search. If the value is in the array, then 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 constant? You observe that the number of job postings per day has remained stable, but the number of applicants has been 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 usefulness for analysis? Additionally, describe common problems in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• Write a function to calculate sample variance from a list of integers. Create a function that takes a list of integers and returns the sample variance, rounded to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

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

• Write a function to return the median value of a list 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. Example input: li = [1, 2, 2]. Example output: median(li) -> 2.

• What are the drawbacks of the given student test score data layouts? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems 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, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which random forest generates its ensemble of trees. Additionally, discuss the advantages of using random forest compared to logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm, and discuss the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• 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 and have completed a V1 of the model. What metrics would you use to evaluate the model's accuracy and validity?

Are you ready to make a significant impact in the world of renewable energy? Clean Power Research is leading the energy transformation with innovative cloud software solutions. By joining our team, especially as a Software Engineer, you'll not only develop cutting-edge SaaS products but also contribute to solutions that drive the renewable energy sector forward. 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 software engineer 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!