Adobe Data Science Research Intern Interview Questions & Experience Guide

Interview Preparation
,
1

Adobe Data Science Research Intern Interview Questions & Experience Guide

2

Company Name: Adobe

Position: Data Science Research Intern

Application Process: Applied through the company’s career portal.

Interview Rounds:

  • Round 1 - Tech Lead Interview:
    • Questions Asked:
      • Introduction and background.
      • Discussion on previous projects related to data science.
      • Technical questions on machine learning algorithms and their applications.
      • A problem-solving question related to data analysis.
    • Your Approach:
      • Tried to explain my projects clearly and how they align with the role.
      • Answered technical questions to the best of my knowledge but felt unprepared due to the unexpected nature of the round.
    • Outcome: Did not advance to the next round. Received feedback to focus more on practical applications of theoretical concepts.

Preparation Tips:

  • Clarify the interview format beforehand to avoid surprises.
  • Brush up on both theoretical and practical aspects of data science, especially if the role involves research.

Conclusion:
The experience was a bit unexpected due to the round being different from what was communicated. However, it highlighted the importance of being adaptable and well-prepared for any type of interview round. For future candidates, I’d recommend confirming the interview structure in advance and preparing comprehensively.

3

Company Name: Adobe

Position: Data Science Research Intern

Application Process: Applied online through the company’s career portal.

Interview Rounds:

  • Round 1 - Technical Interview:
    • Questions Asked:
      1. Introduction: Tell me about yourself, your experience, and what you like.
      2. Two data structure coding questions based on hashmaps and sets.
      3. Machine Learning questions: Linear regression concepts.
      4. Discussion on projects mentioned in the resume.
    • Your Approach:
      • For the introduction, I kept it concise, focusing on my academic background, relevant projects, and interests in data science.
      • For the coding questions, I first explained my thought process before writing the code, ensuring clarity.
      • For the ML questions, I discussed the assumptions, mathematical formulation, and practical applications of linear regression.
      • During the resume discussion, I highlighted my contributions and learnings from past projects.
    • Outcome: Cleared the round successfully.

Preparation Tips:

  • Practice coding problems on data structures, especially hashmaps and sets.
  • Revise fundamental ML concepts like linear regression thoroughly.
  • Be prepared to discuss your resume in detail, focusing on your role and the impact of your projects.

Conclusion:
The interview was a great learning experience. I felt confident in my preparation, especially in the coding and ML sections. For future candidates, I’d recommend practicing problem-solving aloud and being ready to explain your thought process clearly.

4

Company Name: Adobe

Position: Data Science Research Intern

Application Process: Applied through the company’s career portal.

Interview Rounds:

  • Round 1 - Technical Interview with Engineering Manager:

    • Questions Asked:
      • Design questions related to data science projects.
      • Discussion on deep learning state-of-the-art papers.
    • Your Approach:
      • Tried to recall and discuss relevant papers and design principles, though I wasn’t entirely up-to-date in that specific area.
    • Outcome:
      • The interviewer was nice, but I felt a bit rusty on the topics.

  • Round 2 - Technical Interview with Senior Data Scientist:

    • Questions Asked:
      • More in-depth design questions.
      • Further discussion on recent advancements in deep learning.
    • Your Approach:
      • Attempted to connect my existing knowledge to the questions, but realized I needed more preparation in this domain.
    • Outcome:
      • The interviewer was professional, but I didn’t perform as well as I’d hoped due to gaps in my knowledge.

Preparation Tips:

  • Stay updated with the latest research papers in your field.
  • Brush up on design principles and practical applications of data science.

Conclusion:
The interviewers were friendly and the process was smooth, but I realized the importance of staying current with research and design trends. For future candidates, I’d recommend dedicating time to review recent advancements and practice articulating your thoughts clearly.

5

Company Name: Adobe

Position: Data Science Research Intern

Application Process: I got shortlisted after applying, and the recruiter emailed me to schedule the interview dates.

Interview Rounds:

  • Round 1 - Technical Interview:
    • Questions Asked:
      • Implement the k-means algorithm as a coding question.
      • Probability-related questions.
      • Detailed questions about my resume, focusing on past projects and experiences.
    • Your Approach:
      • For the k-means implementation, I walked through the logic step-by-step and coded it efficiently.
      • For probability questions, I relied on fundamental concepts and applied them logically.
      • For resume-based questions, I highlighted my relevant projects and explained my contributions clearly.
    • Outcome: The interviewer seemed satisfied with my responses, and I moved forward in the process.

Preparation Tips:

  • Focus on understanding core algorithms like k-means thoroughly, as they might ask for implementations.
  • Brush up on probability and statistics, as they are common in data science interviews.
  • Be prepared to discuss your resume in detail, especially any projects or research work.

Conclusion:
The interview was quite resume-oriented, so make sure you know your projects inside out. The coding question was straightforward, but practicing algorithm implementations beforehand helped. Overall, it was a good learning experience!

6

Company Name: Adobe

Position: Data Science Research Intern

Application Process: Applied through the company’s career portal.

Interview Rounds:

  • Round 1 - Technical (Coding):

  • Questions Asked:

    • Given a dataset, how would you preprocess it for a machine learning model?
    • Implement a function to detect outliers in a dataset.
    • Explain the time complexity of your solution.

  • Your Approach:

    • Discussed the importance of data cleaning, normalization, and handling missing values.
    • Wrote a Python function using the IQR method for outlier detection.
    • Analyzed the time complexity as O(n) for the outlier detection function.

  • Outcome: Passed to the next round.

  • Round 2 - Technical (Statistics):

  • Questions Asked:

    • Explain the Central Limit Theorem and its significance.
    • How would you test the hypothesis that a new feature improves model performance?
    • Discuss the difference between parametric and non-parametric tests.

  • Your Approach:

    • Provided a clear explanation of the Central Limit Theorem with an example.
    • Suggested using A/B testing or paired t-tests for hypothesis testing.
    • Differentiated between parametric (e.g., t-test) and non-parametric tests (e.g., Wilcoxon).

  • Outcome: Successfully cleared the round.

Preparation Tips:

  • Brush up on data preprocessing techniques and outlier detection methods.
  • Revise fundamental statistics concepts like hypothesis testing and the Central Limit Theorem.
  • Practice coding problems related to data manipulation and analysis.

Conclusion:
The interview process was well-structured and focused on both coding and statistical knowledge. Preparing thoroughly for technical rounds and being clear in explanations helped. Future candidates should focus on practical implementations of theoretical concepts.

7

Company Name: Adobe

Position: Data Science Research Intern

Application Process: Applied online through the company’s career portal.

Interview Rounds:

  • Round 1 - Video Interview with Hiring Manager:

    • Questions Asked:
      • Walk me through your resume.
      • Describe a challenging project you worked on and how you overcame obstacles.
      • Why are you interested in this role at Adobe?
      • Behavioral questions like teamwork and problem-solving scenarios.
    • Your Approach:
      • Prepared a concise summary of my resume, focusing on key projects and skills.
      • Used the STAR method for behavioral questions to structure my answers clearly.
      • Researched Adobe’s work in data science to align my answers with their goals.
    • Outcome: Passed to the next round.

  • Round 2 - Video Interview with Team Members:

    • Questions Asked:
      • Deep dive into a resume project (tools, methodologies, outcomes).
      • Business case related to an Adobe service (e.g., how to improve user engagement using data science).
    • Your Approach:
      • Prepared a detailed explanation of my project, including challenges and learnings.
      • For the business case, structured my answer by defining the problem, proposing solutions, and discussing potential impact.
    • Outcome: Awaiting results.

Preparation Tips:

  • Review your resume thoroughly and be ready to discuss any project in detail.
  • Practice behavioral questions using the STAR method.
  • Research Adobe’s products and think about how data science can add value to them.

Conclusion:
The interviews were insightful and focused on both technical and behavioral aspects. I felt well-prepared but could have practiced more case studies to improve my confidence. For future candidates, I’d recommend balancing technical prep with business acumen.