Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Analytical Background
The candidate's analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
Airbnb-Specific Data Patterns
Domain-specific data modeling and analytics patterns used in Airbnb-scale product analytics. Covers data schema design, event and transaction patterns, feature engineering templates for predictive models, cohort and lifecycle analytics, geospatial and temporal data patterns, price and demand forecasting signals, AB testing data patterns, and data quality, governance, and lineage considerations relevant to Airbnb data.
Dashboard and Data Visualization Design
Principles and practices for designing, prototyping, and implementing visual artifacts and interactive dashboards that surface insights and support decision making. Topics include information architecture and layout, chart and visual encoding selection for comparisons trends distributions and relationships, annotation and labeling, effective use of color and white space, and trade offs between overview and detail. The topic covers interactive patterns such as filters drill downs tooltips and bookmarks and decision frameworks for when interactivity adds user value versus complexity. It also encompasses translating analytic questions into metrics grouping related measures, wireframing and prototyping, performance and data latency considerations for large data sets, accessibility and mobile responsiveness, data integrity and maintenance, and how statistical concepts such as statistical significance confidence intervals and effect sizes influence visualization choices.
Business Impact Measurement and Metrics
Selecting, measuring, and interpreting the business metrics and outcomes that demonstrate value and guide decisions. Topics include high level performance indicators such as revenue decompositions, lifetime value, churn and retention, average revenue per user, unit economics and cost per transaction, as well as operational indicators like throughput, quality and system reliability. Candidates should be able to choose leading versus lagging indicators for a given question, map operational KPIs to business outcomes, build hypotheses about drivers, recommend measurement changes and define evaluation windows. Measurement and attribution techniques covered include establishing baselines, experimental and quasi experimental designs such as A B tests, control groups, difference in differences and regression adjustments, sample size reasoning, and approaches to isolate confounding factors. Also included are quick back of the envelope estimation techniques for order of magnitude impact, converting technical metrics into business consequences, building dashboards and health metrics to monitor programs, communicating numeric results with confidence bounds, and turning measurement into clear stakeholder facing narratives and recommendations.
Metrics Selection and Dashboard Storytelling
Focuses on selecting metrics and designing dashboards and reports that directly support stakeholder decision making. Candidates should be able to identify distinct audiences and the specific decisions each audience must make, choose actionable metrics rather than vanity metrics, and balance leading indicators with lagging indicators as well as strategic metrics with operational metrics. This topic covers defining key performance indicators and targets and justifying each metric by the decision it enables, setting data freshness requirements and update cadence, and ensuring instrumentation and data quality to make metrics reliable. It includes dashboard architecture and visual narrative design such as layering from high level summaries to detailed drill down, tailoring views for executives, managers, and operational teams, selecting appropriate visualizations and annotations to guide interpretation, and enabling root cause analysis. Reporting practices are covered, including formatting, distribution channels, and alerting. Governance and metric definition topics include creating a single source of truth, assigning ownership, documenting definitions, and change control. Candidates must also recognize metric interactions and common pitfalls that can make metrics misleading such as aggregation bias, sampling issues, correlation versus causation, and perverse incentives, and propose mitigations. Interview questions typically ask candidates to design metric sets and dashboards for hypothetical scenarios, explain why metrics were chosen based on decisions they support, and describe cadence, distribution, drilling, and governance approaches.
Data Analysis and Requirements Translation
Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural choices.
Metrics and KPI Fundamentals
Core principles and practical fluency for defining, measuring, and interpreting metrics and key performance indicators. Candidates should be able to select meaningful metrics aligned to business objectives rather than vanity metrics, explain the difference between a metric and a target, and distinguish leading indicators from lagging indicators. Coverage includes decomposing complex outcomes into actionable component metrics, writing precise metric definitions such as what counts as a daily active user and monthly active user, calculating common metrics such as engagement rate, churn rate and conversion rates, establishing baselines and sensible targets, and interpreting signal versus noise including awareness of statistical variability. Also includes using segmentation and cohort analysis to diagnose metric movements and recommending two to three meaningful metrics for a hypothetical problem with justification and action plans.
Data Investigation and Root Cause Analysis
Techniques and a structured process for diagnosing metric changes and anomalies using quantitative evidence complemented by qualitative signals. Candidates should demonstrate how to validate that an observed change is a real signal and not noise or a reporting or instrumentation problem by checking data quality, event counts, sampling, and pipeline integrity. Describe slicing and decomposition strategies such as cohort segmentation, geography and platform segmentation, feature level analysis, time series decomposition to separate trend and seasonality, funnel and velocity analysis, retention analysis, and variance analysis. Explain how to form, prioritize, and test hypotheses; design diagnostic queries and tests using structured query language; and correlate metric changes with product releases, experiments, marketing activity, or external events. Include how to combine quantitative findings with qualitative research such as user interviews, session replay, logs, and support tickets to strengthen causal inference. Finally, cover communicating concise findings and actionable recommendations to stakeholders, creating reproducible queries and monitoring dashboards or alerts, and mentoring junior analysts on a systematic investigation approach.
Trend Analysis and Anomaly Detection
Covers methods for detecting and interpreting deviations in metric behavior over time and determining whether changes reflect real product or user behavior versus noise. Topics include baseline establishment, seasonality and holiday effects, time series decomposition, smoothing and aggregation choices, statistical detection techniques such as control charts, z scores, EWMA and CUSUM, thresholding strategies, and modern algorithmic approaches like isolation forest or LSTM-based detectors. Also covers visualization and dashboarding practices for communicating trends, setting sensible alerting rules, triage workflows for investigating anomalies, and assessing business impact to prioritize fixes or rollbacks.