Geli (Growing Energy Labs, Inc.) provides innovative software and business solutions for designing, connecting, and operating energy storage systems ranging from residential to utility-scale. As a subsidiary of Hanwha Q CELLS, Geli aims to create an "Internet of Energy" where renewable energy sources are optimized and interconnected through advanced software.

Geli is searching for a passionate Machine Learning Operations (ML Ops) Engineer to join their Data Science team. In this role, you will maintain and deploy forecasting algorithms crucial to Geli's software. Responsibilities include building robust ML pipelines, collaborating with cross-functional teams, and continuously improving model performance.

This interview guide on Interview Query will walk you through the interview stages, commonly asked questions, and provide valuable tips to prepare for your machine learning engineering journey at Geli. Ready to make a difference in renewable energy? Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Geli as a Machine Learning Engineer. Whether you were contacted by a Geli 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 Geli 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 Geli Machine Learning 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 Geli Machine Learning 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 ML pipelines, forecasting algorithms, and performance monitoring.

You may also be asked to work on a take-home assignment involving Python coding, machine learning model implementation, and basic debugging. Apart from these, your proficiency in machine learning libraries such as Sklearn, Keras, and TensorFlow, as well as cloud technologies like Docker, Kubernetes, and AWS, may also be assessed during the round.

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 Geli office or virtually if required. Your technical prowess, including ML modeling and operational 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 Machine Learning Engineer role at Geli.

Quick Tips For Geli Machine Learning Engineer Interviews

• Plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Geli interview include:

• Understand Renewable Energy Concepts: Geli's core focus is on energy storage systems and renewable energy. Familiarize yourself with basic concepts of renewable energy and energy storage, as well as specific technologies like microgrids.

• Be Collaborative: Geli values collaboration among team members. Highlight your experience working in diverse teams and your ability to communicate effectively with different departments.
• Demonstrate Learning Agility: The energy industry is rapidly evolving, and Geli seeks individuals who are willing to learn and adapt quickly. Be prepared to discuss instances where you've shown a proactive approach to learning new technologies or solving complex problems.

Typically, interviews at Geli vary by role and team, but commonly Machine Learning 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?

• 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 suspect anything unusual 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. Would you consider this result suspicious?

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

• Why might job applications 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 issues 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.

• How to find the median in a list where over 50% of the elements are the same? Given a sorted list of integers where more than 50% of the list is the same 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 and formatting changes needed for messy datasets? You have student test scores in two different layouts (dataset 1 and dataset 2). 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 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 both before deployment and after it is in use?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss why one might 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 asked 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? Assume you have built a V1 of a spam classifier for emails. What metrics would you use to monitor the model's accuracy and validity?

What is the average salary for a Machine Learning Engineer at Geli?

Q: What does Geli do? Geli (Growing Energy Labs, Inc.) provides software and business solutions for designing, connecting, and operating energy storage systems ranging from residential to utility-scale. The company offers a hardware-agnostic software platform for deploying advanced energy projects.

Q: What is Geli’s vision? Geli envisions a cleaner, better world running on 100% renewable energy. They aim for a future with less reliance on non-renewable power, where electricity can be sourced locally and software optimizes the use of solar, wind, and battery storage.

Q: What does the Machine Learning Operations (ML Ops) Engineer role entail? The ML Ops Engineer at Geli will support the deployment and maintenance of forecasting algorithms central to Geli’s software. Responsibilities include building and maintaining ML pipelines, deploying algorithms into production, implementing monitoring systems, and continuously improving model performance.

Q: What qualifications are required for the ML Ops Engineer position? Candidates should have a strong foundation in computer science and software engineering, experience with Python (3.6+), familiarity with machine learning algorithms, and previous work with libraries like sklearn, Keras, and Tensorflow. The role also requires collaborative skills, a proactive approach, and a willingness to learn about the growing energy industry.

Q: Why should I consider working at Geli? Working at Geli offers the chance to make a difference in the renewable energy sector, engage with a dynamic team, and gain exposure to transformative business models and technologies. The company promotes a diverse and inclusive work environment and is currently mostly remote.

Joining Geli means becoming part of a dynamic team committed to revolutionizing the energy storage industry with innovative software solutions. As an ML Ops Engineer at Geli, you will play a critical role in shaping the future of renewable energy, working at the intersection of data science, software engineering, and DevOps. If this excites you and you're passionate about leveraging your skills to make a substantial impact, we encourage you to prepare thoroughly for your interview.

If you want more insights about the company, check out our main Geli 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 Geli machine learning 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!