Knewin is a leading company specializing in media monitoring and digital intelligence. Known for its advanced technological solutions, Knewin is a pivotal player in transforming how businesses access and utilize information.

Stepping into a role at Knewin as a Machine Learning Engineer means being at the forefront of innovation and data-driven decision-making. This position demands strong expertise in machine learning, data analysis, and algorithm development. As part of the team, you will be working on cutting-edge projects that require continuous learning and adaptation to new technologies.

This guide by Interview Query will help you navigate through the interview process at Knewin, providing you with crucial insights, commonly asked questions, and helpful tips to prepare you for a successful interview. Let's get started on your journey to joining Knewin!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Knewin as a Machine Learning Engineer. Whether you were contacted by a Knewin 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 Knewin 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 Knewin 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 Knewin 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 Knewin's data systems, ETL pipelines, and ML algorithms.

In the case of machine learning roles, take-home assignments regarding data set analysis, model building, and evaluation are incorporated. Apart from these, your proficiency in areas such as Python programming, data manipulation, feature engineering, and machine learning model selection 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

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 Knewin 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 Machine Learning Engineer role at Knewin.

Quick Tips For Knewin Machine Learning Engineer Interviews

You should 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 Knewin interview include:

• Know Your Models: Knewin questions are likely to focus on machine learning models and algorithms. Make sure you understand various ML models, their applications, and their limitations.
• Be Data Driven: Knewin’s machine learning interviews assess how well you can derive insights and build models from data. Brush up on your knowledge of data preprocessing, feature engineering, and model evaluation.
• Understand the Business: Knewin values understanding how to apply machine learning solutions to solve real-world business problems. Make sure you can discuss past experiences and how your work impacted business decisions or outcomes.

Typically, interviews at Knewin vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• How would you measure the effectiveness of extra pay for delivery drivers during peak hours? You work at a food delivery company and want to assess the impact of offering extra pay to delivery drivers during peak hours. How would you measure whether this incentive effectively meets consumer demand?

• Create a function rain_days to calculate the probability of rain on the nth day after today. The probability that it will rain tomorrow depends on whether it rained today and yesterday. If it rained both days, there's a 20% chance it will rain tomorrow. If it rained one of the days, there's a 60% chance. If it rained neither day, there's a 20% chance. Given it rained today and yesterday, write a function to calculate the probability it will rain on the nth day after today.

• What are the assumptions of linear regression? List and explain the key assumptions that must be met for linear regression analysis to be valid.

Interested in diving deeper into Knewin's Machine Learning Engineer interview process? Check out our main Knewin Interview Guide, where we've covered various potential interview questions. We've also compiled thorough interview guides for roles like software engineer and data analyst to help you understand Knewin’s comprehensive interview stages for different positions.

At Interview Query, we provide you with the necessary tools, resources, and strategic insights to master your interview. Our goal is to equip you with confidence and knowledge to ace every question and challenge in your Knewin Machine Learning Engineer interview.

Explore all our company interview guides to better prepare yourself. If you have any questions, feel free to reach out to us.

Good luck with your interview!