Netflix Data Scientist (Staff Level) Interview Preparation Guide
Netflix's Data Scientist interview process evaluates technical expertise, statistical knowledge, product sense, experimental design, and cultural alignment. The process spans approximately 4-6 weeks and includes phone screening rounds, a technical assessment, and multiple onsite interview loops where you'll interact with data scientists, engineers, managers, and executives. For Staff level, the interview emphasizes strategic business impact, mentorship capabilities, advanced technical depth, and organizational influence.[1][2][3]
Interview Rounds
Recruiter Screening
What to Expect
Initial phone conversation with a Netflix recruiter lasting approximately 30 minutes.[3] This round focuses on your background, motivation for Netflix, career trajectory, and basic qualifications. The recruiter will assess culture fit, verify your experience level, and discuss logistics including preferred locations and compensation expectations. For Staff level, the recruiter will be particularly interested in your track record of high-impact projects, leadership experience, and strategic contributions. This is primarily a qualification check before proceeding to deeper technical evaluation.[1][3]
Tips & Advice
Be concise and compelling in describing your background. Have 2-3 concrete examples of high-impact projects ready that demonstrate business value, not just technical complexity. Show genuine interest in Netflix as a company and in data science specifically. For Staff level, emphasize your strategic contributions, mentorship of junior data scientists, and impact on team processes. Ask thoughtful questions about the role and team to show engagement. Research Netflix's recent product launches, personalization strategy, and data culture before the call.[1][3]
Focus Topics
Motivation & Interest in Netflix
Clearly articulate why Netflix specifically, not just any tech company. Reference Netflix's data-driven approach to personalization, content decisions, or experimentation. Show you understand Netflix's business model and how data science contributes. For Staff level, discuss how Netflix's scale, complexity, or philosophy aligns with your career aspirations.
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Study Questions
Mentorship & Team Leadership
For Staff level, have ready specific examples of how you've mentored junior data scientists, influenced team processes, or contributed to hiring and onboarding. Describe a junior team member you developed and their growth trajectory. Explain how you've improved team practices, standardized approaches, or scaled team capabilities.
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Study Questions
Netflix Culture & Freedom & Responsibility
Demonstrate understanding of Netflix's unique culture of Freedom & Responsibility. Show how you've operated autonomously, made decisions with incomplete information, and taken ownership in past roles. Share an example where you identified a problem, took initiative without waiting for approval, and drove results.
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Career Narrative & Strategic Impact
Craft a compelling 2-3 minute narrative of your career progression emphasizing how each role increased your scope of responsibility and impact. For Staff level, focus on how you've transitioned from individual contributor to influencing strategy, mentoring teams, and driving organizational initiatives. Articulate specific examples where your data science work directly influenced business decisions or product strategy.
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Hiring Manager Technical Screen
What to Expect
Phone conversation with the hiring manager lasting approximately 30 minutes,[3] occurring 1 week after the recruiter screen. This round goes deeper into your technical background, statistical knowledge, and past project experience. The hiring manager will ask detailed questions about your work experiences, the tools and techniques you use, how you approach complex problems, and how you've driven business decisions with data. They're assessing whether you have the right technical depth and problem-solving approach for the role.[4] For Staff level, expect questions about your most complex projects, how you've influenced technical direction, and your depth in specific domains like experimentation or machine learning.
Tips & Advice
Prepare 3-4 detailed project examples using the STAR framework, focusing on projects that required strong statistical reasoning, dealing with ambiguity, or significant business impact. Be ready to discuss metrics—what you measured, how you validated results, and the business outcome. For each project, explain your technical approach, the tools you used (Python, SQL, statistical methods), and what you learned. For Staff level, choose examples that show you influenced others, improved processes, or tackled ambiguous strategic problems. Discuss trade-offs you made and why. Have specific numbers for impact (increased retention by X%, reduced cost by Y%, etc.). Be prepared to discuss your current tech stack comfort level and gaps you're working to close.[1][4]
Focus Topics
Complex Problem-Solving & Ambiguity
Share examples of problems where the right approach wasn't obvious. Describe how you broke down an ambiguous problem, gathered requirements, and decided on a solution approach. For Staff level, show how you helped define the problem itself, not just solved a pre-defined problem. Discuss how you handled stakeholder misalignment on the problem definition.
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Study Questions
Technical Tool Expertise & Engineering Practices
Clearly state your proficiency levels with Python, R, SQL, and relevant ML frameworks (TensorFlow, scikit-learn, etc.). Be honest about what you use regularly versus what you know conceptually. Discuss your experience with production systems, scalability challenges, and engineering best practices you've employed or advocated for.
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Statistical Rigor & A/B Testing Fundamentals
Be prepared to discuss your approach to statistical validation, hypothesis testing, and experimental design at a conceptual level. Explain how you handle multiple comparisons, statistical power, and sample size calculations. Share an example where statistical rigor prevented a wrong conclusion. Discuss your understanding of common pitfalls in online experimentation.[1][2]
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Project Impact & Metrics-Driven Thinking
Master the ability to clearly articulate how your data science projects translated into business outcomes. Describe your approach to selecting metrics, measuring success, and communicating impact to non-technical stakeholders. For Staff level projects, demonstrate how you set project scope, prioritized what to measure, and influenced stakeholder expectations around uncertainty and trade-offs.
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Technical Assessment Phone Screen
What to Expect
Technical interview lasting 1-2 hours[3] conducted via phone or video, occurring 1-2 weeks after the hiring manager screen. This round tests your practical technical skills across SQL, Python, statistics, and potentially machine learning. You may be asked to write queries analyzing metrics (e.g., retention), solve coding problems in Python, answer conceptual statistics questions, or work through algorithmic challenges.[1] The interviewer is looking for clear thinking, proper problem-solving approach, and ability to communicate your reasoning.
Tips & Advice
Practice writing clean, well-commented SQL queries that efficiently analyze data. Be comfortable with window functions, CTEs, and subqueries. Write Python code that is readable and handles edge cases. Explain your approach before coding. For statistics questions, show your reasoning and be comfortable with basic distributions, hypothesis testing, and A/B test calculations. If given a coding problem, verbalize your approach, discuss time/space complexity, and explain trade-offs. For Staff level, interviewers expect efficient solutions and may probe on scalability of your approach. Test your solutions with examples. Prepare by practicing LeetCode-style medium difficulty problems and SQL optimization exercises.[1][3]
Focus Topics
Algorithmic Problem Solving
Practice solving algorithmic problems of medium difficulty. Problems typically involve arrays, strings, graphs, or dynamic programming. Focus on clearly explaining your approach, discussing complexity, and optimizing solutions. Show your debugging process if you get stuck. Time yourself to ensure you can solve a medium problem in 30-40 minutes.
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Study Questions
Statistical Concepts & Hypothesis Testing
Deeply understand probability distributions (normal, binomial, Poisson), hypothesis testing framework (Type I/II errors, p-values), confidence intervals, and power analysis. Be able to calculate required sample size for an experiment and interpret experiment results. Discuss common statistical pitfalls and how to avoid them. Understand multiple comparison corrections and false discovery rates. For Staff level, discuss tradeoffs between statistical rigor and business speed.
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Advanced SQL & Data Analysis
Master complex SQL queries including window functions (ROW_NUMBER, RANK, LAG/LEAD), Common Table Expressions (CTEs), self-joins, and subqueries. Be able to solve real-world analytics problems like calculating retention cohorts, churn metrics, user lifetime value, or time-series analysis. Understand query optimization and when different approaches have different performance characteristics. For Staff level, be prepared to discuss indexing strategies, partition schemes, and how to handle massive datasets.
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Python Programming & Data Manipulation
Write clean, well-structured Python code. Be comfortable with pandas for data manipulation, numpy for numerical computing, and general algorithmic problem solving. Understand time and space complexity implications of your code choices. Write code that handles edge cases and is robust. For Staff level, discuss code organization, testability, and how you'd scale a solution from small to large datasets.
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Onsite Round 1: Core Technical Skills & SQL Deep Dive
What to Expect
First onsite interview (or early onsite phase) lasting approximately 1-1.5 hours with data scientists and/or data engineers from Netflix.[1][3] This round focuses on technical depth in SQL, data analysis, and Python. You'll be asked to solve real analytics problems that Netflix data scientists face. Problems may involve building complex queries to compute metrics, analyzing data to identify trends, or implementing analysis in Python. The interviewers want to assess your ability to work with large, complex datasets and derive insights efficiently.[1]
Tips & Advice
Come prepared to work through realistic analytics problems. Think about Netflix's business—they care about subscription, retention, content performance, user engagement. Problems often involve: (1) computing retention or churn cohorts, (2) analyzing A/B test results, (3) identifying anomalies in metrics, (4) calculating user lifetime value or similar business metrics. Write your SQL incrementally, test with sample data, and explain your approach. For Staff level, show you're thinking about data quality, edge cases, and scalability. If asked to discuss or implement ML components, focus on practical applicability rather than theoretical sophistication. Discuss how you'd validate your analysis before presenting to stakeholders.[1][3]
Focus Topics
Data Quality & Validation
Discuss how you validate data quality before analysis. What anomalies or issues do you look for? How do you handle missing data or outliers? Discuss your approach to sanity-checking query results. For Staff level, discuss how you'd set up monitoring for data quality and how you've established data quality standards on teams.
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Study Questions
A/B Test Analysis & Interpretation
Be able to extract data from an A/B test (control versus treatment groups), calculate the metrics for each, compute statistical significance, and interpret results. Calculate confidence intervals and p-values. Discuss what to do if a test is underpowered or shows conflicting results. Understand multiple testing corrections if analyzing many metrics.[1]
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Retention & Churn Analysis
Learn to calculate retention cohorts, churn rates, and understand retention curves. Be able to write SQL to identify subscribers by cohort and their behavior over time. Understand how retention is typically measured in subscription businesses. Be prepared to analyze how different user segments show different retention patterns and what factors might influence retention.
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Metrics Definition & Calculation
Understand how to precisely define business metrics (DAU, subscription metrics, engagement metrics) and implement them in SQL. Discuss edge cases like how to handle cancellations mid-period, trial users, or different subscription types. Practice writing queries that correctly aggregate behavior over time periods. For Staff level, discuss how metrics should be versioned and governed as they evolve.
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Onsite Round 2: Experimental Design & Causal Inference
What to Expect
Onsite interview lasting approximately 1-1.5 hours with senior data scientists or a dedicated experimentation team member. This round is heavily focused on experimental design, A/B testing, and causal inference. You'll discuss how Netflix should design experiments to answer specific business questions, what metrics to track, how many users to include, how long to run tests, and how to interpret results.[1] The interviewer will present realistic business scenarios and ask how you'd approach them experimentally. For Staff level, expect deeper questions about experimental methodology, potential biases, and how to handle complex scenarios.
Tips & Advice
Master the fundamentals of experimental design: randomization, power analysis, sample size calculations, and multiple comparison corrections. Be comfortable discussing different experimental designs (A/B test, multi-armed bandit, incrementality testing) and when to use each. Understand common biases in experiments (selection bias, survivorship bias, seasonal effects) and how to mitigate them. Prepare to discuss Netflix-specific scenarios: how would you design an experiment for a UI change? A pricing change? A new feature rollout? For Staff level, discuss how you'd influence experimentation strategy, set up governance, or scale experimentation across teams. Discuss tradeoffs between statistical rigor and business speed. Have opinions on when to stop an experiment early and why.[1][2]
Focus Topics
Experiment Design for Netflix Use Cases
Think through designing experiments for realistic Netflix scenarios: measuring impact of a UI change, a new recommendation algorithm, a pricing change, a content release strategy, or a feature rollout. Discuss what the key metrics would be, how long to run the test, how to randomize fairly, and what could go wrong. For Staff level, discuss how experimental results would influence product decisions and what stakeholder concerns you'd address.
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Study Questions
Experimentation Pitfalls & Bias
Discuss common pitfalls in experimentation: peeking at results early (inflates false positive rate), multiple comparison issue, survivorship bias, seasonality, novelty effects, and carryover effects. How would you mitigate each? Discuss how Netflix's unique context (subscription business, content release calendar, regional differences) creates specific challenges.
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Netflix A/B Testing Fundamentals
Understand the principles of randomized controlled experiments at Netflix scale. Learn how Netflix typically structures A/B tests, including how users are randomized (often at account level, sometimes device level), how metrics are monitored, and how long tests typically run. Understand Netflix's approach to variant selection and the importance of randomization to avoid bias.
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Statistical Power & Sample Size
Master calculating required sample size for experiments. Understand how to balance statistical power (typically 80-90%), significance level (alpha, typically 0.05), and the minimum detectable effect size (MDE) you care about. Discuss how confidence intervals relate to power. For Staff level, discuss how to set MDE strategically based on business value of the experiment.
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Causal Inference & Confounding
Understand the difference between correlation and causation. Learn about confounding variables and how randomization addresses them. Discuss situations where randomization isn't possible and what approaches you'd use (matching, propensity score, difference-in-differences, instrumental variables). For Netflix context, discuss examples like measuring the causal impact of a feature or content on user behavior when you can't run a pure A/B test.
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Onsite Round 3: Product Sense, Metrics & Business Impact
What to Expect
Onsite interview lasting approximately 1-1.5 hours with a product manager, business stakeholder, or senior data scientist focused on product strategy. This round evaluates your product intuition, ability to think about business metrics holistically, and capacity to drive business impact.[3] You'll be asked questions like: how would you measure success for a Netflix feature, what metrics would you track for a content strategy, how do you prioritize what to analyze, or how do you balance conflicting metrics? The interviewer is assessing whether you think like a business leader, not just a technician.
Tips & Advice
Approach this round thinking like a product leader, not just a data scientist. When asked to define metrics, think about Netflix's North Star metrics: subscriber growth, engagement, retention, profitability. Understand how different metrics relate (engagement typically drives retention). Be able to articulate tradeoffs—increasing engagement might decrease retention if it's low-quality content, or increasing revenue might hurt subscriber growth. For Staff level, show you understand the full business model: Netflix makes money from subscriptions, retention, and engagement (via ad-supported tier). Share examples of how your analysis influenced product decisions or strategy. Discuss how you'd approach prioritizing multiple projects competing for your time. Show you think about scaling impact—how to go from solving one problem to developing capabilities the whole organization uses.[1][3][4]
Focus Topics
Trade-offs & Holistic Thinking
Understand that Netflix decisions involve trade-offs. Maximizing engagement might hurt retention; maximizing revenue might hurt growth; optimizing for profits might reduce subscriber satisfaction. Discuss how you'd approach a decision where metrics give conflicting signals. For Staff level, discuss how you'd help leadership navigate these tradeoffs and what data-informed recommendations you'd make.
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Study Questions
Storytelling & Communicating Impact
Be able to tell a compelling story about a project and its impact. Structure your narrative around the business question, your approach, key findings, and the business decision made based on your analysis. Practice translating technical analysis into business language. For Staff level, discuss how you've influenced executives or set organizational strategy through your analysis. Show how you've made complex findings digestible and actionable.
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Study Questions
Defining Success Metrics & Product Measurement
Master the ability to take a business objective and define metrics that measure success. For a hypothetical Netflix feature, articulate what you'd measure, how you'd measure it, and what you'd consider a successful outcome. Discuss leading versus lagging indicators. Discuss how to measure long-term impact when you can only observe short-term data. For Staff level, discuss how to govern metrics across teams and ensure everyone is measuring consistently.
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Netflix North Star Metrics & Business Model
Understand Netflix's primary business metrics: subscriber growth, retention, engagement, revenue per member, and profitability. Understand how these metrics interrelate—retention is driven by engagement, both subscribers and engagement affect revenue. For different features or changes, understand which metrics matter most and why. For Staff level, understand how Netflix prioritizes among conflicting objectives and how data science influences that prioritization.
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Onsite Round 4: Leadership, Mentorship & Culture Fit
What to Expect
Final onsite interview lasting approximately 1-1.5 hours with Netflix executives, directors, or senior leaders.[3] This round assesses your leadership capability, cultural alignment, and potential to grow into and beyond the Staff role. You'll be asked about how you've influenced teams, developed junior team members, approached ambiguity, made difficult trade-offs, or shaped organizational practices. The interviewer wants to understand if you're someone who elevates the entire organization, not just executes well individually. Your ability to articulate Netflix's Freedom & Responsibility culture and demonstrate how you embody it is critical.[1]
Tips & Advice
Prepare to discuss your leadership and influence beyond your direct responsibilities. How have you shaped data science practices? Improved team capabilities? Developed junior team members? Influenced product or company decisions through your perspective? Share specific stories that illustrate your character and values. For each, use STAR format. Focus on ambiguity, ownership, and delivering results. Be authentic—Netflix deeply values authenticity and calling out issues when needed. Be prepared to discuss how you'd approach Netflix's Freedom & Responsibility: what does it mean to you? How do you exercise freedom while maintaining responsibility? Discuss how you'd scale your thinking from individual contributor to Staff level impact across teams. Be ready to ask thoughtful questions about Netflix's direction, challenges, and where the company is heading with data science.[3]
Focus Topics
Dealing with Disagreement & Difficult Situations
Share an example where you disagreed with leadership or a peer. How did you handle it? What was the outcome? Netflix values directness and the ability to respectfully disagree. Show you can advocate for your position while remaining open to being wrong. Discuss how you handle feedback or when someone pushes back on your analysis.
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Strategic Impact & Big Picture Thinking
Demonstrate that you think beyond your immediate project. Share examples of how you've influenced strategy, changed team practices, or contributed to organizational decisions. Discuss your perspective on how data science should evolve at Netflix or where the field is heading. For Staff level, discuss how you're thinking about the career arc and where you want to have impact.
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Netflix Culture & Values Alignment
Articulate understanding of Netflix's core values: Freedom & Responsibility, bias toward action, data-driven decision making, directness, and high performance. Share examples of how you've embodied these values. Discuss how you've balanced freedom with responsibility—when have you made an autonomous decision and why was that the right call? When have you escalated something and why? For Staff level, discuss how you reinforce culture on your team.
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Mentorship & Team Development
Articulate your philosophy on developing junior data scientists. Share specific examples of team members you've mentored and their growth. Discuss how you've coached people on difficult problems, given feedback, or helped them develop skills. For Staff level, discuss how you've scaled mentorship (mentoring multiple people), influenced hiring decisions, or contributed to team culture. Discuss how you've built psychological safety and encouraged growth.
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Ownership & Initiative
Share examples where you identified a problem without being asked, took action, and drove results. Discuss how you handle ambiguity—when direction isn't clear, how do you make decisions? For Staff level, discuss how you've shaped the direction of projects or teams through your perspective. Show you're comfortable making calls without consensus and can justify your reasoning.
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Frequently Asked Data Scientist Interview Questions
Sample Answer
Sample Answer
SELECT r.device_id,
r.ts,
r.value,
s.hour_sum
FROM readings r
LEFT JOIN LATERAL (
SELECT SUM(value) AS hour_sum
FROM readings r2
WHERE r2.device_id = r.device_id
AND r2.ts >= r.ts - INTERVAL '1 hour'
AND r2.ts <= r.ts
) s ON true
ORDER BY r.device_id, r.ts;SELECT device_id, ts, SUM(value) OVER (
PARTITION BY device_id
ORDER BY ts
RANGE BETWEEN INTERVAL '1 hour' PRECEDING AND CURRENT ROW
) AS hour_sum
FROM readings;Sample Answer
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from scipy.spatial import cKDTree
from scipy.special import logit
from math import isnan
def propensity_score_matching(df, treatment_col, outcome_col, covariates):
# Fit logistic regression for propensity score
X = df[covariates].astype(float)
y = df[treatment_col].astype(int)
model = LogisticRegression(max_iter=1000, solver='lbfgs')
model.fit(X, y)
ps = model.predict_proba(X)[:,1]
df = df.copy()
df['ps'] = ps
# logit PS (avoid 0/1)
eps = 1e-6
df['logit_ps'] = logit(np.clip(ps, eps, 1-eps))
caliper = 0.2 * df['logit_ps'].std()
# Split treated / control
treated = df[df[treatment_col]==1].reset_index()
control = df[df[treatment_col]==0].reset_index()
# Build KD-tree on control logit_ps for fast NN search
control_vals = control[['logit_ps']].values
tree = cKDTree(control_vals)
matches = {}
used_control_idx = set()
for i, row in treated.iterrows():
val = row['logit_ps']
# query neighbors in increasing order until we find unused within caliper
dists, idxs = tree.query([val], k=min(50, len(control)), distance_upper_bound=caliper)
found = False
# tree.query returns inf idx if not found; handle list vs scalar
if np.isscalar(dists):
dists = [dists]; idxs = [idxs]
for dist, idx in zip(dists, idxs):
if np.isinf(dist): break
if idx in used_control_idx: continue
used_control_idx.add(idx)
matches[row['index']] = control.loc[idx,'index'] # original indices
found = True
break
# if no match within caliper, leave unmatched
# Build matched dataset
matched_pairs = []
for t_idx, c_idx in matches.items():
treated_row = df.loc[t_idx]
control_row = df.loc[c_idx]
matched_pairs.append((treated_row, control_row))
matched_t = pd.DataFrame([t for t,c in matched_pairs])
matched_c = pd.DataFrame([c for t,c in matched_pairs])
matched_df = pd.concat([matched_t.assign(matched_treated=1), matched_c.assign(matched_treated=0)], ignore_index=True)
# Compute ATT
# average of treated outcomes minus their matched control outcomes
if len(matched_pairs)==0:
raise ValueError("No matches found within caliper")
treated_outcomes = np.array([t[outcome_col] for t,c in matched_pairs], dtype=float)
control_outcomes = np.array([c[outcome_col] for t,c in matched_pairs], dtype=float)
ATT = np.mean(treated_outcomes - control_outcomes)
# SMD function
def smd(a, b):
a = np.array(a, dtype=float); b = np.array(b, dtype=float)
pooled_sd = np.sqrt((a.var(ddof=1) + b.var(ddof=1)) / 2.0)
if pooled_sd==0 or isnan(pooled_sd): return 0.0
return (a.mean() - b.mean()) / pooled_sd
# SMD table before and after
smd_rows = []
for cov in covariates:
before = smd(df[df[treatment_col]==1][cov], df[df[treatment_col]==0][cov])
after = smd(matched_t[cov], matched_c[cov]) if len(matched_pairs)>0 else np.nan
smd_rows.append({'covariate':cov, 'smd_before':abs(before), 'smd_after':abs(after)})
smd_table = pd.DataFrame(smd_rows)
return {
'matched_dataframe': matched_df,
'ATT': ATT,
'smd_table': smd_table,
'caliper': caliper,
'ps_model': model
}Sample Answer
Sample Answer
Sample Answer
Sample Answer
Sample Answer
WITH ev AS (
SELECT
pageview_id,
user_id,
ts,
page_type,
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY ts) AS rn
FROM clickstream
),
-- restrict to only events that are A/B/C to reduce join size
ev_abc AS (
SELECT * FROM ev WHERE page_type IN ('A','B','C')
)
SELECT DISTINCT e1.user_id
FROM ev_abc e1
JOIN ev_abc e2
ON e1.user_id = e2.user_id
AND e1.rn < e2.rn
AND e2.ts > e1.ts
AND e2.page_type = 'B'
JOIN ev_abc e3
ON e1.user_id = e3.user_id
AND e2.rn < e3.rn
AND e3.ts > e2.ts
AND e3.page_type = 'C'
WHERE e1.page_type = 'A'
AND e3.ts <= e1.ts + INTERVAL '30 minutes';Sample Answer
Sample Answer
Recommended Additional Resources
- Glassdoor - Netflix Data Scientist interview reviews and questions
- Blind (teamblind.com) - Anonymous Netflix employee experiences and interview feedback
- Levels.fyi - Netflix compensation and interview process details
- "Trustworthy Online Controlled Experiments" by Kohavi, Tang, and Xu - definitive guide on experimentation at scale
- "Causal Inference: The Mixtape" by Scott Cunningham - comprehensive guide to causal inference methods
- "Applied Statistics for Data Analysis" - covers statistical foundations for business applications
- LeetCode - Medium difficulty SQL and Python problems for technical interview prep
- Mode Analytics SQL Tutorial - practical SQL skill building
- Netflix Technical Blog - research papers and technical posts from Netflix engineers
- A/B Testing course by CXL Institute - comprehensive overview of experimentation methodology
- Subscription business metrics resources - understand Netflix's business model and key performance indicators
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