Alina Bezchotnikova

Principal UX, Product Insights · 17 yrs B2B SaaS · PhD

Read Product Analytics Like a Statistician: 5 Checks

Small n, novelty effects, segment fishing, Simpson's paradox, seasonality.

Run the 5 checks before you trust any metric

Three live examples from real product data, including one drop that looked like 12% and turned out to be nothing.

Tell a real movement from a chart artefact in 5 minutes

The prompt that takes your Amplitude/Mixpanel export and runs all five checks.

Let AI run the checks for you, without inventing numbers

Eliminate uncertainty in shipping AI features

GenAI Product Builder @ Google, ex-Meta

Marily Nika, Ph.D AI/ML

Senior Data, ML and GenAI Scientist

George Zoto

Senior NLP/AI Scientist

August-2026

“Does it work?”... “Is it good enough?”... “Can we ship it?”...How do you answer these questions for AI products? You’re responsible for “running evals” but what does that mean?How do you choose the right metrics, interpret fuzzy results, and make a confident decision?This course gives you a framework to do just that.<ul><li>Map user value to evaluation (eval) objectives so your metrics aren’t abstract. Define success then translate it into measurable criteria.</li><li>Choose metrics you can actually maintain: capability, safety, UX friction, latency, cost and “does this reduce support tickets or increase activation.”</li><li>Set ship/no-ship thresholds you can defend to leadership.</li><li>Build lightweight workflows that work in real teams: human review where it matters, automation where it lasts, documentation that drives decisions.</li><li>Consider domain constraints (e.g., healthcare safety) and know what to avoid: silent failures, misleading proxy metrics and tests that don’t reflect production.</li><li>Tie everything to ROI: impact vs unit cost, eval coverage vs reliability, and the minimum viable monitoring you need post-launch.</li></ul>Experience AI evals through a case-based approach with a real AI product that we evaluate together.

Acquire and develop a critical skill for product managers who are leading and contributing to AI products.

Make confident ship or hold decisions for AI features

Translate user value into clear evaluation goals

Choose the right metrics for capability, safety, UX, and cost

Set defensible thresholds leadership will trust

Build lightweight evaluation workflows teams can maintain

Navigate domain constraints and avoid common failures

PMs leading AI features, growth, or platform initiatives

PMs who partner with ML teams and want to set evaluation standards

PMs who need to make clear “ship or hold” calls without doing the engineering

Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.

Principal Data Scientist @ Ontra 

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Shane Butler

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Data Science Leader |Ex-Microsoft

Microsoft

eBay

Grammarly

Superhuman

Sravya Madipalli

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Head of Data @ Ontra AI | Ex-LinkedIn

LinkedIn

Pinterest

Hai Guan

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

Cohort 3

Cohort 4

As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship. Analytical independence - the ability to validate your own ideas and make data-informed decisions - remains the missing skill in most builders' toolkits.AI can conduct the analysis, but it can't tell you what to ask or whether the answer makes sense. That requires analytical judgment.This course teaches you to think like a senior Product Data Scientist. You'll learn the frameworks used at FAANG companies to frame problems, choose the right metrics, diagnose root causes, execute the analysis, and turn insights into decisions.-&gt; <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">Join AI Builders Slack Community!!</a> &lt;-What makes this different: No SQL. No stats lectures. Five weeks of hands-on real scenarios. You'll build end-to-end, metric specs, deep dives, experiment plans, stakeholder readouts.The result: You become analytically self-sufficient. The last dependency removed from your toolkit. Make data-informed decisions in hours, not weeks.

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.

Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights

Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.

Built on a $1.3B P&L and a U.S. Army mission. For Practitioners whose CEO measures them on the metric.

$1.3B P&L@Home Depot · CEO · U.S. Army

The Home Depot

U.S. Army

Cadence

Stanford University

TechCrunch

Rocky Sharma

$1.3 Bn P&L at Home Depot. Iron Man's helmet for U.S. Army. CEO at 3 companies


July 2026

Oct 2026

It is 3:30 PM Tuesday. Your CFO just asked how the AI feature you shipped last quarter will protect next quarter's budget. You have a demo.Millions call themselves AI Product Builders. Anyone can vibe-code a prototype in a weekend. What none of that ships is Product Judgment, the discipline that turns a demo into a product an executive will fund, defend, and sign for.This course teaches four skills and the Product Judgment that ties them together. Product-Market Fit is table stakes. Above it: Executive Metrics Fit and Mission Fit, the three altitudes every AI product must clear before funding.Rocky teaches the skills he practiced across a $1.3B P&amp;L at Home Depot, the U.S. Army bomb-disposal suit (Iron Man's helmet in real life), and the founding team of Amazon Business. Think like an Executive. Explore like an Inventor. Pitch like an Operator. Apply like a Product Builder. Then the Mission Steward, the role executives sign to keep.Four sessions. 15 live hours. A vetted AI business case Rocky reviews live, including three hours with a Fortune 10 AI leader.

Millions call themselves AI Product Builders. Few practice Product Judgment.

Build Product Judgment: the discipline all 3 audiences the course serves need.

Move from feature-factory practitioners to the metric driven path your VP funds.

Pitch an AI investment your CEO approves on the spot.

Set AI strategy tied to the metric your executive is on the hook for.

Run Product exploration that proves the solution before any code ships.

Frame ROI and risk in the language a CFO will actually fund.

You've shipped at least one product or feature — this course builds on that foundation, not from scratch.

3–7+ years of Product Management experience

You've used ChatGPT, Claude, or Gemini at work or personally — so we can skip the basics and go deeper.

Familiarity with at least one AI tool

Week 2 prototyping sessions run on Claude (claude.ai — Pro plan, USD 20/month). You will need the paid plan to build and test it

Access to Claude's paid plan (USD 20/month)

AI Product Builders. You use AI tools daily. Turn the title AI Product Builder into a practice: Product Judgment as a discipline.

Product Managers. Feature-brief PM work is cost-center-cuttable now. Earn Product Judgment: the discipline that keeps your seat.

Operators (CPO, VP Product, founder-CEO). One wrong call costs a quarter or millions. Install Product Judgment across your team.

Product judgement improves one way: make your reasoning visible, put a number on it, test it against reality, and get more calls right.

CPO · ex-Amazon, Intuit, PayPal

Amazon

PayPal

Intuit

PropertyGuru

Viveck P Kumar

20 years building products at Amazon, Intuit, PayPal, PropertyGuru. Now a CPO.

Founding Cohort. Oct 2026

You are expected to make consequential bets before the evidence is complete. You weigh data, challenge assumptions, debate trade-offs. But outcomes arrive months later with execution, timing and luck tangled together. The result gets measured. The quality of the original judgement rarely does.Product sense is a story. Product judgement is a track record.The course turns judgement into a feedback loop. Value Architecture sharpens what you see: the assumptions behind why customers will adopt, stay and pay, and where that value is most likely to break. Then you write down what you believe will happen, how confident you are, and by when. That is all a forecast is. When reality answers, your calls are scored, and you see exactly what you overestimated, what you missed, and how to make the next call better.AI raises the stakes. Building is becoming fast and cheap, so there will be more bets, made faster. When any team can ship, the advantage shifts from building faster to choosing better. Execution is becoming abundant. Judgement is becoming the constraint.You leave with the loop, and your first honest measure of how often your confident calls come true.

Senior product leaders are paid for judgement. Few can prove theirs is improving

Diagnose why customers actually adopt, stay, and pay, on your own product

Write the falsifiable thesis your product is silently betting on

 Design the four jobs every product must do: believe, switch, stay, compound

Forecast where your product breaks, with probabilities, horizons, evidence

Get scored: two quarterly reviews where your forecasts resolve

Leave with the artifacts: your Value Architecture Brief and a live ledger

The Lab runs on your actual bets. Your Brief and forecasts are built on your product, not case studies.

A live product with real decisions in front of you

You will log real forecasts and defend them. That only works if the bets are yours to make.

Decision rights over product bets

A scored ledger rewards honesty, not bravado.

Comfort being wrong on the record

Founders. Your product bets can change the company's trajectory. Price your conviction honestly before the market does.

CPOs, VPs, Heads of Product. You own a portfolio of bets and a room that expects you to be right. Take home a discipline your team inherits.

Product Directors, senior product leaders with decision rights. You own bets, not backlogs, and have live product decisions in front of you.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Product managers who drive strategy, development, and launch of products.

Using data and metrics to measure performance, run experiments, and inform decisions.

Business analysts defining requirements and bridging business and technical teams.

Read Product Analytics Like a Statistician: 5 Checks

What you'll learn

Why this topic matters

You'll learn from

Alina Bezchotnikova