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      Entrevista de Data Scientist/Machine Learning Engineering

      11 dic 2023
      Candidato de entrevista anónimo
      Oferta rechazada
      Experiencia neutra
      Entrevista normal

      Solicitud

      Envié una solicitud electrónica. El proceso duró 3 semanas. Acudí a una entrevista en Proxify en nov 2023

      Entrevista

      First stage - Hr discussion Second stage - Online Assesment Third Stage - Technical Interview The process is pretty generic but the technical interview could be better as its more like a repeat of the online assessment but with a person

      Preguntas de entrevista [1]

      Pregunta 1

      The online assessment has four sections: SQL Queries: Writing SQL queries. MCQ for Data Science: Multiple-choice questions related to data science. MCQ for Statistics & Probability: Multiple-choice questions on statistical concepts and probability. Python Coding: Writing Python code to solve a coding problem. The Technical Interview stage involves an interview with a Karat interviewer. This interview is not a typical discussion about the work you've done; rather, it's more like an extension of the online assessment. The interviewer asks questions similar to queries, poses statistics questions (e.g., about p-values and appropriate distributions for different scenarios), and requires you to solve a Python coding question. During this stage, you are allowed to use Google, but it's crucial to communicate openly about what you are looking up. Copy-pasting from sources like ChatGPT is discouraged, and the interview is conducted via video.
      Responder pregunta
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      Respuesta de Proxify
      2y
      Thank you for your detailed feedback on our interview process. It's great to hear that you found the structure encompassing a broad range of relevant areas. We acknowledge your point regarding the similarity between the online assessment and the technical interview. The intention behind this design is to ensure a consistent, comprehensive evaluation of technical skills, allowing candidates to demonstrate their abilities in both a self-guided and an interactive environment. However, we understand this approach might feel repetitive and overlook the opportunity to explore a candidate's unique experiences and problem-solving methods in greater depth. Our goal is to not only assess technical proficiency but also to gain a better understanding of a candidate's thought process, creativity, and ability to apply their knowledge to real-world problems. We believe in fostering a dialogue where candidates can share more about their work, the challenges they've faced, and how they've overcome them rather than focusing solely on technical testing. Furthermore, we appreciate your note on using resources like Google during the interview. This policy aims to mimic real-world scenarios where problem-solving often involves researching and applying information. However, we also want to ensure that this practice doesn't detract from the assessment's integrity or from evaluating a candidate's ability to think critically and solve problems independently. We're committed to making our interview process as effective, fair, and enjoyable as possible for all candidates. Your feedback is crucial for us in this ongoing process, and we're grateful for the opportunity to improve. If you have any further suggestions or want to discuss your experience, please don't hesitate to reach out. We value open communication and always look for ways to better align our process with the needs and expectations of our candidates.

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