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Customized Interview Preparation Materials for Devica Verma

Behavioral Questions

Easy:

  1. Team Collaboration: Can you describe a time when you worked on a project with a cross-functional team? What was your role, and how did you ensure effective communication and collaboration?

    • Talking Points:
      • Reference your experience at Vegas.com where you collaborated with cross-functional teams to resolve 42 critical website bugs.
      • Discuss the importance of clear communication and teamwork in achieving a 7-day average bug resolution time.

  2. Mentoring Experience: How do you approach mentoring or guiding others in a professional setting?

    • Talking Points:
      • Highlight your role as a Teaching Assistant for Applied Machine Learning at Columbia University.
      • Mention your experience as a Judge & Mentor at TreeHacks, Stanford University, mentoring hackers on advanced ML algorithms.

Medium:

  1. Conflict Resolution: Describe a situation where you faced a conflict within your team. How did you handle it, and what was the outcome?

    • Talking Points:
      • Provide an example from your time at JobTarget or Vegas.com where differing opinions arose and explain how you facilitated a resolution.
      • Emphasize your communication skills and ability to find common ground.

  2. Leadership: Can you share an experience where you took the lead on a project or initiative? What were the challenges, and how did you overcome them?

    • Talking Points:
      • Discuss founding and leading the GRATE community, growing it from 0 to 90 members and conducting biweekly status meetings.
      • Highlight your organizational and leadership skills in managing community growth.

Hard:

  1. Strategic Decision Making: Can you describe a time when your analytical skills had a significant impact on a project or decision-making process?

    • Talking Points:
      • Reference the development of a quantitative model at Vegas.com to assess the impact of site outages on KPIs.
      • Explain how this model facilitated informed data-driven decision-making processes.

  2. Adapting to Change: Tell me about a time when you had to quickly adapt to significant changes in a project. How did you manage to stay on track?

    • Talking Points:
      • Provide an example from your work experience where project requirements or timelines changed unexpectedly.
      • Discuss your ability to stay flexible and maintain focus on delivering results.

Technical Questions

Easy:

  1. Machine Learning Basics: Can you explain the difference between supervised and unsupervised learning?

    • Talking Points:
      • Define supervised learning with examples like classification and regression.
      • Define unsupervised learning with examples like clustering and dimensionality reduction.

  2. Python Programming: How would you handle missing data in a dataset using Python?

    • Talking Points:
      • Mention techniques such as removing rows/columns with missing values, filling missing values using mean/median/mode, and using interpolation.
      • Reference your experience with data preprocessing in projects like the prediction of health inspection scores of restaurants.

Medium:

  1. Model Evaluation: How do you evaluate the performance of a machine learning model?

    • Talking Points:
      • Discuss metrics like accuracy, precision, recall, F1 score, and ROC-AUC.
      • Provide examples from your projects, like achieving an MAE of 7.87 using XGBoost Regressor.

  2. Data Science Techniques: Can you describe a time when you used feature selection techniques to improve a model’s performance?

    • Talking Points:
      • Reference your project on predicting health inspection scores where you used correlation heatmaps and Random Forest Regressor for feature selection.
      • Explain the impact of feature selection on model performance.

Hard:

  1. Deep Learning: How would you approach building a CNN for image classification? Can you discuss any specific architecture you have used?

    • Talking Points:
      • Discuss your experience with Inceptionv3 and custom CNN architectures in your cancer metastases detection project.
      • Explain transfer learning and hyperparameter tuning techniques you used to improve model performance.

  2. Advanced Machine Learning: How do you handle imbalanced datasets in classification problems?

    • Talking Points:
      • Mention techniques like resampling (oversampling minority class or undersampling majority class), using different evaluation metrics (precision-recall curve), and algorithmic approaches (SMOTE).
      • Provide examples where you might have encountered and addressed imbalanced datasets.

Key Achievements and Skills to Highlight

  • Technical Expertise:

    • Proficiency in Python, R, SQL, TensorFlow, Keras, and other relevant tools and frameworks.
    • Experience with machine learning, deep learning, natural language processing, and time series forecasting.

  • Project Experience:

    • Successfully led the development of high-performing ML models at Vegas.com, resulting in significant user conversion and revenue uplift.
    • Developed a novel CNN architecture during your internship at JobTarget, achieving high predictive accuracy.

  • Leadership and Mentorship:

    • Demonstrated leadership as the Founder & President of the GRATE community and as a Judge & Mentor at TreeHacks.
    • Experience mentoring students as a Teaching Assistant at Columbia University.

  • Problem Solving and Decision Making:

    • Created a quantitative model to assess site outages' impact on KPIs, facilitating informed decision-making at Vegas.com.
    • Proactively resolved critical website bugs, improving user ratings and reducing bug resolution time.

By preparing answers around these customized questions and talking points, Devica Verma can effectively showcase her qualifications and align her experiences with the specific requirements of the Machine Learning Engineer, Core Ranking role at Reddit, Inc.