ds-interview-mcp-server

Design, analyze, and interpret A/B tests and controlled experiments. Calculate sample sizes, run power analysis, detect common pitfalls (peeking, multiple comparisons, network effects), and apply advanced techniques like CUPED variance reduction and switchback experiments. For product experimentation, feature launches, and causal inference.

Structured frameworks and strategies for behavioral interviews at top tech companies. Covers STAR method, story banking, leadership principles mapping, and common behavioral question categories. Tailored for data analyst and product analytics roles at Meta, Google, Amazon, Airbnb, Netflix, and similar companies.

Churn analysis and prediction for product analytics. Covers churn definition frameworks, survival analysis, RFM segmentation, predictive modeling, and retention intervention strategies. Includes SQL churn queries, Python survival curves, and feature engineering for churn prediction models.

Perform cohort analysis and retention measurement for product analytics. Build retention curves, triangle retention tables, heatmaps, and survival analysis. Covers SQL retention queries, Python visualization, and frameworks for diagnosing and improving retention across different product types.

Build and analyze conversion funnels for product analytics. Covers funnel construction, drop-off diagnosis, segmented funnel analysis, and conversion optimization frameworks. Includes SQL funnel patterns, Python visualization, and interview approach for funnel questions.

Define, measure, and optimize product metrics. Apply AARRR pirate metrics, HEART framework, North Star metrics, and Goal-Signal-Metric (GSM) process. For product analytics, growth measurement, feature launch evaluation, and KPI design at tech companies.

Frameworks and structured approaches for product sense interview questions. Covers CIRCLES, metric definition, root cause analysis, feature prioritization, and product case study approaches. Essential for PM and product analytics interviews at Meta, Google, Airbnb, and other top tech companies.

Python patterns for product data analysis using pandas, numpy, and visualization libraries. Covers data wrangling, aggregation, time series, visualization best practices, and common anti-patterns. Practical reference for interview coding rounds and daily analytical work.

Write advanced SQL for product analytics including window functions, CTEs, funnel queries, sessionization, cohort analysis, and growth accounting. Covers query optimization, interview patterns, and real-world analytical SQL at scale. For data analyst interviews, dashboard building, and ad-hoc product investigations.

Applied statistics for product data analytics including hypothesis testing, confidence intervals, power analysis, distributions, Bayesian methods, and common statistical pitfalls. Covers practical application of statistics in A/B testing, metric analysis, and data-driven decision making for product teams.