The demand for skilled data scientists continues to surge, with top tech companies like Netflix, Apple, LinkedIn, and Glassdoor leading the way in innovative data-driven decision making. Interviews at these organizations are renowned for their challenging and insightful questions, designed to test not only technical expertise but also analytical thinking and problem-solving abilities. In this article, we’ll dive deep into the top 5 data scientist interview questions asked at these companies, provide thorough answers, and explain the key concepts you need to master to excel in your interviews.

Top 5 Data Scientist Interview Questions from Netflix, LinkedIn, Glassdoor, and Apple

1. Netflix: How Would You Build and Test a Metric to Compare Two User's Ranked Lists of Movie/TV Show Preferences?

Recommendation systems are at the core of Netflix's product. Evaluating the similarity between two users’ ranked lists of movies or TV shows is essential for collaborative filtering, user clustering, and content personalization. This question tests your knowledge of ranking metrics, similarity measures, and validation techniques.

Understanding the Problem

Given two users, each with a ranked list (ordered by preference) of movies/TV shows, how can we quantitatively measure how similar or different their preferences are? The metric should account for both the items in the lists and their order.

Possible Approaches

• Kendall’s Tau: Measures the correspondence between two rankings by counting concordant and discordant pairs.
• Spearman’s Rank Correlation: Computes the Pearson correlation coefficient on the ranked variables.
• Jaccard Similarity: Measures the overlap (ignoring order) between the two lists.
• Normalized Discounted Cumulative Gain (NDCG): Popular in information retrieval to evaluate ranking quality.

Recommended Metric: Kendall’s Tau

Kendall’s Tau is a robust metric for comparing ranked lists, as it directly measures the degree of similarity in the orderings. Given two ranked lists \(A\) and \(B\), Kendall's Tau (\(\tau\)) is defined as:

\[ \tau = \frac{(\text{Number of Concordant Pairs}) - (\text{Number of Discordant Pairs})}{\frac{1}{2}n(n-1)} \] where \(n\) is the number of items ranked by both users.

Implementation Example

import scipy.stats

def kendalls_tau(rank_a, rank_b):
    # rank_a and rank_b must be lists of ranks for shared items
    return scipy.stats.kendalltau(rank_a, rank_b).correlation

Testing the Metric

• Unit Tests: Evaluate on synthetic data where expected similarity is known.
• Edge Cases: Empty lists, lists with ties, lists of different lengths.
• Real Data Validation: Compare metric outputs with user similarity as perceived by human evaluators.
• Statistical Significance: Use permutation testing to check if observed similarities are significant.

Extensions

• If lists are of unequal length or contain different items, consider extending with set-based metrics (e.g., Jaccard for overlap, then Kendall’s Tau for the order of the intersection).
• For partial rankings, use weighted variants or imputation strategies.

Summary

To compare two users’ ranked preferences, use a rank correlation metric like Kendall’s Tau. Validate your metric with synthetic and real data, and extend as needed for partial or unequal lists.

2. Yammer: You Are Compiling a Report for User Content Uploaded Every Month and Notice a Spike in Uploads in October, Particularly Pictures. What Might You Think is the Cause of This, and How Would You Test It?

This question assesses your ability to detect anomalies in data, hypothesize causes, and design tests to validate those hypotheses. It reflects real-world data investigative work.

Approach

1. Data Exploration: Confirm the spike is real (not due to reporting errors or system bugs).
2. Hypothesis Generation: Why might picture uploads spike in October?
3. Testing Hypotheses: Use data analysis and experiments to validate.

Step 1: Data Validation

• Check for duplicates or system changes in October.
• Compare with previous years – is this a recurring pattern?
• Segment by user demographics, regions, or device types.

Step 2: Hypotheses

• Seasonal Events: October has Halloween (especially in US and Western countries), a time when people upload costume and event photos.
• Product/Feature Launch: Was there a new feature or campaign launched in October encouraging picture uploads?
• External Factors: Media events, viral challenges, or holidays driving uploads.

Step 3: Testing Hypotheses

• Time Series Analysis: Plot uploads by day. Does the spike align with Halloween or a specific date?
• Content Analysis: Use image recognition or keyword analysis to detect Halloween costumes, pumpkins, etc.
• Campaign Analysis: Cross-reference with marketing calendars or release notes.
• User Survey: Ask users about their upload motivations in October.

Statistical Testing

To confirm statistical significance of the October spike:

\[ H_0: \text{The mean number of picture uploads in October is equal to other months.} \] \[ H_a: \text{The mean number of picture uploads in October is greater than other months.} \]

Apply a two-sample t-test or ANOVA:

from scipy.stats import ttest_ind

# uploads_october, uploads_other_months are arrays of counts
stat, p_value = ttest_ind(uploads_october, uploads_other_months, alternative='greater')

Summary

The spike is likely due to Halloween or a campaign. Validate by analyzing patterns, correlating with external events, and confirming with statistical tests.

3. LinkedIn: Find the Second Largest Element in a Binary Search Tree

This is a classic data structures and algorithms interview question, commonly asked to assess your understanding of tree traversal and properties of binary search trees (BST).

Key Concepts

• In a BST, for any node, all left descendants are less, and all right descendants are greater.
• The largest element is the right-most node.
• The second largest is either:
  • The parent of the largest node (if the largest node has no left child).
  • The right-most node in the left subtree of the largest node (if it has a left child).

Algorithm

1. Traverse right until you reach the right-most node.
2. If it has a left child, find the right-most node in its left subtree.
3. If not, its parent is the second largest.

Python Implementation

class Node:
    def __init__(self, key):
        self.key = key
        self.left = None
        self.right = None

def find_second_largest(root):
    if root is None or (root.left is None and root.right is None):
        return None  # No second largest

    parent = None
    current = root
    while current.right:
        parent = current
        current = current.right

    # Case 1: Largest has left subtree
    if current.left:
        current = current.left
        while current.right:
            current = current.right
        return current.key

    # Case 2: Parent is second largest
    return parent.key

Complexity

• Time Complexity: \(O(h)\), where \(h\) is the height of the tree.
• Space Complexity: \(O(1)\) (iterative solution).

Edge Cases

• Tree with only one node – no second largest.
• Balanced and unbalanced trees.

Summary

To find the second largest element in a BST, traverse to the largest node and handle based on its left subtree. This tests your grasp of tree traversal and BST properties.

4. Glassdoor: How Would You Test if Survey Responses Were Filled at Random by Certain Individuals, as Opposed to Truthful Selections?

Detecting random or fraudulent survey responses is a common challenge in data science, especially in online surveys. This question evaluates your ability to use statistical analysis and data validation techniques.

Approach

1. Define “Random Response”: Responses that do not reflect genuine opinions or behaviors, but are selected arbitrarily.
2. Identify Patterns Indicative of Randomness:
   • Uniform distribution of answers.
   • Short response times.
   • Inconsistencies or contradictions.
   • Repeated or patterned answers.
3. Statistical Testing: Use hypothesis tests to differentiate random from truthful responses.

Techniques

• 1. Response Distribution:
  • For each respondent, compare their answer distribution to the overall population or expected distribution.
  • Use the Chi-Square Goodness of Fit Test:
    \[ H_0: \text{The respondent's distribution matches the expected distribution.} \]
• 2. Response Time Analysis:
  • Calculate the time taken to answer each question. Extremely fast completions may indicate random answering.
• 3. Consistency Checks:
  • Insert “attention check” questions to see if respondents are paying attention.
  • Look for logical inconsistencies in answers.
• 4. Pattern Detection:
  • Detect repeated patterns (e.g., always picking the first option).
  • Calculate entropy of responses; low entropy may indicate non-random but patterned answers.

Example: Chi-Square Test for Uniformity

from scipy.stats import chisquare

# Suppose survey_q_counts is a list of response counts per option for one respondent
expected = [total_responses / num_options] * num_options
stat, p_value = chisquare(survey_q_counts, expected)
if p_value < 0.05:
    print("Response distribution is unlikely to be random.")
else:
    print("Cannot rule out randomness.")

Summary

To detect random survey responses, analyze distribution, timing, consistency, and patterns in the data. Use statistical tests such as the Chi-Square test, and design surveys with built-in attention checks to flag suspicious respondents.

5. Apple: How Do You Take Millions of Users with 100's of Transactions Each, Amongst 10k's of Products, and Group the Users Together in Meaningful Segments?

This question is about user segmentation at scale—one of the most impactful applications of data science in technology and retail. Apple uses such techniques to tailor experiences, recommend products, and understand customer behavior.

Approach Overview

1. Feature Engineering: Transform massive raw transaction data into meaningful user features.
2. Dimensionality Reduction: Reduce feature space for more effective clustering.
3. Clustering/Segmentation: Apply clustering algorithms to group similar users.
4. Interpretation and Validation: Ensure clusters are actionable and meaningful.

Step 1: Feature Engineering

• Aggregate Transactions: For each user, derive features such as:
  • Total spend
  • Number of unique products purchased
  • Average order value
  • Purchase frequency
  • Top product categories
  • Recency, frequency, monetary (RFM) metrics
  • Sparsity vector: binary vector indicating whether a product was purchased
• Handling High Dimensionality: With 10,000+ products, consider:
  • Reducing to product categories
  • Matrix factorization (SVD, PCA)
  • Embedding users/products using techniques like t-SNE, UMAP, or autoencoders

Step 2: Dimensionality Reduction

To avoid the “curse of dimensionality”, apply dimensionality reduction:

• PCA (Principal Component Analysis): Projects data into a lower-dimensional space preserving variance.
• t-SNE/UMAP: For visualization and capturing non-linear relationships.
• Autoencoders: Neural networks that learn compact representations of users.

from sklearn.decomposition import PCA

pca = PCA(n_components=50)
reduced_features = pca.fit_transform(user_product_matrix)

Step 3: Clustering Algorithms

• K-Means Clustering: Fast and scalable for large datasets. Use the elbow method to choose \(k\).
• Hierarchical Clustering: Good for exploratory analysis, not scalable for millions of users.
• DBSCAN: Density-based, finds arbitrarily shaped clusters, robust to noise.
• Gaussian Mixture Models: Probabilistic model, allows for soft clustering.
• Deep Clustering: Combine
  • Deep Clustering: Combines deep learning (autoencoders) with clustering algorithms to handle extremely high-dimensional, sparse user-product matrices, which is common in large-scale e-commerce or app ecosystems like Apple’s.

  Step 4: Example Workflow – Scalable User Segmentation

  Let’s walk through a practical, scalable workflow for grouping millions of users:

  1. Data Preparation:
     • Build a user-product purchase matrix, where rows are users and columns are products (values indicate number of purchases).
     • Optionally, aggregate products into categories or use embeddings to reduce dimensionality.
  2. Feature Engineering:
     • Extract RFM (Recency, Frequency, Monetary) metrics for each user.
     • Compute diversity of purchases, favorite categories, and average time between transactions.
     • Normalize features to ensure comparability (e.g., using StandardScaler).
  3. Dimensionality Reduction:
     • Use PCA or autoencoders to reduce the feature matrix to manageable dimensions (e.g., down to 20-100 components).
  4. Clustering:
     • Apply scalable clustering algorithms. KMeans is widely used due to its efficiency and implementation in distributed platforms like scikit-learn and Spark MLlib.
  5. Cluster Validation & Interpretation:
     • Use silhouette score, Davies–Bouldin index, or domain-specific KPIs to assess cluster quality.
     • Profile clusters by summarizing mean and median of key features per cluster.
  6. Business Integration:
     • Translate clusters into actionable segments (e.g., “Bargain Hunters”, “Premium Loyalists”, “Impulse Buyers”).
     • Work with product and marketing teams to leverage segments for personalization and targeting.

  Step 5: Sample Python Code

  
  from sklearn.preprocessing import StandardScaler
  from sklearn.decomposition import PCA
  from sklearn.cluster import KMeans
  
  # Assume user_product_matrix is (num_users x num_products)
  scaler = StandardScaler()
  user_features = scaler.fit_transform(user_product_matrix)
  
  # Reduce dimensions
  pca = PCA(n_components=50)
  user_features_reduced = pca.fit_transform(user_features)
  
  # Cluster users
  kmeans = KMeans(n_clusters=10, random_state=42)
  clusters = kmeans.fit_predict(user_features_reduced)
  
  # Assign clusters to users
  user_clusters = {user_id: cluster for user_id, cluster in zip(user_ids, clusters)}
  

  Step 6: Scaling to Millions of Users

  • Leverage distributed computing frameworks (Spark, Dask) for large-scale data processing.
  • Use approximate algorithms (e.g., MiniBatchKMeans) to handle memory and computation constraints.
  • Store and serve clusters using scalable databases (e.g., BigQuery, Redshift).

  Cluster Validation Metrics

  • Silhouette Score: Measures how similar users are within a cluster vs. other clusters. Range: [-1, 1].
  • Davies–Bouldin Index: Lower values indicate better clustering.
  • Business Metrics: Retention, conversion, or engagement rates per segment.

  Advanced: Embeddings & Deep Learning for Segmentation

  For very high-dimensional or sparse data, consider training an autoencoder to learn compact, non-linear user embeddings. You can then cluster users in this embedding space for more nuanced segmentation.

  
  from keras.layers import Input, Dense
  from keras.models import Model
  
  input_dim = user_product_matrix.shape[1]
  encoding_dim = 64
  
  input_layer = Input(shape=(input_dim,))
  encoded = Dense(encoding_dim, activation='relu')(input_layer)
  decoded = Dense(input_dim, activation='sigmoid')(encoded)
  
  autoencoder = Model(input_layer, decoded)
  encoder = Model(input_layer, encoded)
  
  autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
  autoencoder.fit(user_product_matrix, user_product_matrix, epochs=10, batch_size=256, shuffle=True)
  
  user_embeddings = encoder.predict(user_product_matrix)
  # Now cluster user_embeddings as before
  

  Summary

  To segment millions of users based on product transactions, extract meaningful user features, reduce dimensionality, apply scalable clustering algorithms, and interpret segments in a business context. Advanced techniques like embeddings and deep clustering can provide even richer, more actionable groupings at scale.

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  Summary Table: Top 5 Data Scientist Interview Questions & Concepts

  Company	Question	Key Concepts Tested	Strategy/Answer Summary
  Netflix	How to build and test a metric to compare two users’ ranked lists of preferences?	Ranking metrics, similarity measures, statistical validation	Kendall’s Tau, Spearman’s Rank, NDCG; validate with synthetic & real data
  Yammer	How to investigate a spike in picture uploads in October?	Anomaly detection, hypothesis testing, data exploration	Check for external events (e.g., Halloween), product launches, statistical tests
  LinkedIn	Find the second largest element in a Binary Search Tree	Tree traversal, BST properties, algorithms	Traverse to right-most node, handle left subtree, iterative approach
  Glassdoor	Test if survey responses were filled at random	Statistical testing, pattern detection, data validation	Chi-square test, attention checks, response timing, entropy analysis
  Apple	How to segment millions of users by transactions across 10k+ products	Feature engineering, dimensionality reduction, clustering (K-Means, embeddings)	Aggregate features, PCA/autoencoders, cluster at scale, interpret segments

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  Conclusion

  Cracking data science interviews at top tech companies like Netflix, Apple, LinkedIn, and Glassdoor requires mastery not just of technical tools, but of a strategic, analytical mindset. By understanding ranking metrics, anomaly investigation, data structures, statistical testing, and scalable machine learning, you’ll be prepared for both classic and innovative interview challenges. Use the detailed answers and code examples above to practice and deepen your understanding—these are the very skills and concepts that set apart successful data science candidates.

  For more interview tips, deep dives, and resources, check our other articles on advanced machine learning, product analytics, and data-driven decision making!

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