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Data Science (DS) Knowledge Graph

This is a system-level skill tree dynamically generated based on your cloud Memory. By mastering these knowledge nodes, you will build a solid bridge to roles like DS, DA, and PA.

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DS
DA
PA
Scanning Memory to build graph...
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Target Company Interview Guides

Field guides for top-tier companies and core specialized roles. Subscribed premium users can query local/cloud agents with verified prompts to initiate tailored preparation loops.

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Meta Interview Blueprint

Covered Roles

Data Scientist - Product AnalyticsData Scientist - Core Data ScienceResearch Scientist - Experimentation

Core Focus Area

Core evaluation covers product metrics design, A/B testing details (Type I/II errors, sample size calculation, network effects), and non-experimental causal inference methods. Strongly emphasizes Product Sense.

Preparation Hacks

  • Master sample spillover bias correction for network effects (e.g. cluster randomization).
  • Get familiar with non-experimental causal modeling techniques like Double Machine Learning or Synthetic Control.
  • Prepare a structured root-cause analysis framework for drops in key North Star metrics of Meta core products.

🤖 Agent Prompt Command

🔒 Premium Only

The full blueprint, prompt commands, and reference files for this company are locked. Subscribe to Premium to unlock full resources and interact with local MCP Agent.

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Guides & Materials Log

2026-06-24Quantitative Analyst (v2.0)
🔍Google

Updated 'Google QA Variance Reduction Guide': Added chalkboard derivations of multi-linear regression coefficients during CUPED variance reduction adjustment.

2026-06-23Data Scientist (v1.5)
💳Stripe

Updated 'Stripe Credit Fraud Detection Evaluation': Added Bayesian derivations of cost-sensitive loss matrix weights and decision boundary adjustments.

2026-06-21Data Scientist (v1.8)
♾️Meta

Updated 'Meta A/B Test Network Spillover Contamination': Added bias evaluation and mathematical proofs of ego-centric cluster randomization on network graphs.

2026-06-17Data Scientist (v1.1)
🏢TikTok

Updated 'Uplift Modeling Causal Attribution': Added theoretical analysis of Causal Forest split criteria for heterogeneous treatment effects estimation.

2026-06-12merchant pricing (v1.0)
💳Stripe

Added 'Stripe Merchant Rate Elasticity Analysis': Outlines Bayesian hierarchical modeling guides to estimate fee elasticity for mid-to-long tail merchants.

Local Knowledge Base Architecture Guide (Local Template Modules)

Coupled with the cloud skill tree above, when you initialize the knowledge base locally using MCP, the following 6 core modules will be generated automatically. You can use these to build your exclusive local knowledge engine. Click on the cards to expand domain-specific insights.

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Target Companies

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Used for organizing background checks, core businesses, tech stacks, and past interview experiences of your target companies. E.g., categorize OpenAI or Google's interview styles.

ToC Internet Giantse.g., Meta, Netflix. Focuses on extremely rigorous A/B testing, user Growth, and product iteration analysis.
B2B / SaaS CompaniesFocuses on Customer Lifetime Value (LTV) analysis, Churn rate, and commercial monetization strategies.
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Role Analysis

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Displays Job Descriptions, capability models, and common question distributions for target roles (e.g., AS, MLE, DS).

DS-Product (Product Data Scientist)Focuses on product analysis, A/B Testing experimental design, core metric systems, and attribution analysis.
DS-Algorithm/ModelingFocuses on Machine Learning predictive models (e.g., churn rate, LTV prediction) and Causal Inference.
DE (Data Engineer)Focuses on building Data Pipelines, ETL processes, and Data Warehouse architecture design and optimization.
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Interview Records

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Track and record practical post-mortems of every real interview. Includes interviewer backgrounds, difficult questions, and feedback summaries for self-improvement.

Interview Post-mortem TemplateIncludes interviewer background, original questions of each round, my answers, and areas for improvement.
Recording/Transcript SummaryFeed interview transcripts directly via MCP to the Agent to extract summaries automatically.
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Core Projects

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Deeply analyze your personal projects using the STAR principle, document system architecture trade-offs, and prepare for resume deep dives.

S (Situation)Business background and core pain point analysis.
T (Task)Expected quantitative goals and metrics.
A (Action)Tech stack trade-offs, overcoming the biggest technical difficulties.
R (Result)Final quantitative performance and impact.
Deep Dive MockIf traffic is 10x larger, how would you redesign the system?
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Resumes

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Specifically used to track and update your different resume versions (e.g., General, Algorithm-specific) and record Bullet Points highlight materials daily.

Multi-version MaintenanceMaintain custom resume versions for different directions (e.g., Engineering-focused vs. Research-focused).
Highlight Material RepoRecord Bullet Points and highlight materials from daily work, avoiding writer's block when updating resumes.
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Tech Fundamentals

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Contains your overall knowledge framework. From data structures and high-concurrency system design to AI frontier knowledge (like LLM, RAG), accumulate systematic notes here.

Statistics & A/B TestingHypothesis testing, p-value, Statistical Power, avoiding experimental design pitfalls.
SQL & Data ProcessingComplex Window Functions, performance tuning, Pandas/Spark big data processing.
Machine Learning BasicsRegression, Classification, Clustering, Tree models (XGBoost/LightGBM) principles and hyperparameter tuning.