Redefining Data Science Roles: Beyond Technical Skills and Traditional Job Descriptions - ML 155

 In today's episode, Michael Berk and Ben Wilson dive deep into the intricacies of technical interviews for machine learning roles. They discuss the importance of assessing candidates' genuine knowledge of traditional and deep learning models and the value of being candid about one's expertise.They explore how technical skills, particularly in applied machine learning, are evaluated with a focus on their impact on business outcomes. Michael and Ben also address the common misalignments between job descriptions and the actual skills required, stressing the need for problem-solving capabilities and critical thinking over memorized knowledge.Additionally, they delve into the roles within data science—analysts, applied ML specialists, and researchers—highlighting the importance of fitting the right skills to the right job. They also touch on the evolving expectations and frustrations with the current hiring process, offering insights on how it can be improved.Stay tuned as they unpack these topics and more, including valuable tips for showcasing your skills effectively on resumes, and the significance of asking insightful questions during interviews. Whether you’re an aspiring data scientist or a seasoned professional, this episode is packed with practical advice and industry insights you won’t want to miss!SocialsLinkedIn: Ben WilsonLinkedIn: Michael BerkBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.