Clean Power Research is a company at the forefront of the energy transformation, offering cloud software that empowers utilities, energy professionals, and consumers to make informed energy-related decisions. Serving top Fortune 500 utilities and leading renewable energy companies, Clean Power Research is dedicated to expanding its impact through innovative software technologies.

As a Data Scientist at Clean Power Research, you will collaborate with the Data Science team, researchers, software engineers, and product managers to develop and implement machine learning and statistical methods across our enterprise SaaS platform. You'll handle unique datasets, from solar irradiance data to customer interest in clean energy, applying machine learning to solve customer problems and enhance our product offerings.

In this guide, Interview Query will navigate you through the interview process, commonly asked questions, and valuable tips to ace your interview. Dive in and start your journey to becoming a crucial part of Clean Power Research.

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 Data Scientist. 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 data scientist hiring manager may stay 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 data scientist 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 data systems, ETL pipelines, and SQL queries.

In the case of data scientist 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

Following 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 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 scientist role at Clean Power Research.

Quick Tips For Clean Power Research Data Scientist Interviews

• Demonstrate Passion for Clean Technology: Clean Power Research values candidates who are passionate about renewable energy and clean tech. Be prepared to discuss your interest and any related projects you've worked on.
• Showcase Your Data Science Skills: Clearly communicate your experiences and expertise in machine learning, statistical methods, and your ability to handle large datasets. Be ready to provide examples of real-world problems you've solved using these skills.
• Prepare for Behavioral Questions: Apart from technical assessments, Clean Power Research places importance on cultural fit. Be sure to prepare for behavioral questions that assess your teamwork, problem-solving approach, and alignment with the company's mission.

Typically, interviews at Clean Power Research vary by role and team, but commonly Data Scientist 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?

• 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. 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 these 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 job postings per day have 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 the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model for this problem? If you proceed with the decision tree, how would you evaluate its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain how a random forest generates its forest of decision trees. Additionally, discuss why you might choose random forest over other algorithms like logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? You are comparing two machine learning algorithms. In which scenarios would you use a bagging algorithm versus 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? 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 track the accuracy and validity of the model?

What is the average salary for a Data Scientist at Clean Power Research?

Q: What does Clean Power Research do?
Clean Power Research® advances the energy transformation with cloud software that informs, streamlines, and values energy-related decisions for utilities, energy professionals, and consumers. Our solutions help solve the energy industry's toughest challenges.

Q: What makes Clean Power Research an exciting place to work?
At Clean Power Research, you'll transition from building solutions to being part of the solution. Our team includes software and energy experts from top companies like Microsoft, Google, and Oracle. We foster a start-up environment with the stability of an established company, promote work-life balance, and invest in employee growth.

Q: What will a Data Scientist do at Clean Power Research?
As a Data Scientist at Clean Power Research, you'll collaborate with researchers, software engineers, and product managers to develop and implement data science, machine learning, and statistical methods across our enterprise SaaS platform. You'll work with unique datasets, develop machine learning models, and help solve customer problems in the clean energy space.

Q: What are the key qualifications for the Data Scientist role?
We seek an entrepreneurial-minded individual with a passion for clean tech and renewables. Candidates should have 6+ years of combined education and experience in engineering, mathematics, data science, or related fields, strong analytical skills, Python coding expertise, and experience in creating machine learning solutions.

Q: What benefits does Clean Power Research offer?
We offer competitive compensation, including a base salary range of $84,000 to $120,000, performance-based bonuses, and a company equity plan. Additional benefits include medical, dental, vision, life, and disability insurance, 401(k) with matching, paid PTO, sick time, holidays, and more.

If you're passionate about clean energy and have a knack for solving complex problems with data science and machine learning, the Data Scientist role at Clean Power Research could be your perfect fit. This is a unique opportunity to make a tangible impact on the energy industry's transformation while collaborating with a team of veterans from top tech and energy companies. You'll be at the forefront of innovation, leveraging best-in-class datasets and cutting-edge technology to create meaningful solutions.

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. 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 Data Scientist 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!