Data Neighbor

Cohort 1

Cohort 2

Stop measuring outputs. Start measuring outcomes, then convert them to revenue. I’m attaching our simple framework below: "The AI Impact Chain". It’s an intuitive approach anyone can grasp, backed by measurement methods that have been used through decades of data science, statistics, and econometrics.

Guide: Forecast Revenue with AI Eval Metrics

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AI demands a new set of metrics. It requires evolving unreliable deterministic metrics to a systematic analytics process that rigorously ties probabilistic AI output to user value and business impact. In Part I of this guide, we establish the foundational system of metrics required for deterministic applications where the same input reliably produces the same output. In Part II, we walk through how to redesign that measurement framework for probabilistic AI Systems.

Guide to Redesigning Metrics for AI Products

Most AI features fail because the link between model accuracy and business value is broken. This worksheet aligns Product and Data Science teams to map the causal chain: Quality → Behavior → Business Outcome. Includes templates for "Impact Math" forecasting, pre-mortems, and decision gates. Verify your feature's value before you write a single line of code. Download now to turn models into measurable wins.

AI Impact Chain - Planning Worksheet

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

The future belongs to full-stack builders, not narrow specialists. As AI reduces the value of deep domain silos, the people who thrive will be the ones who can work across disciplines and ship.You can already prototype designs, write code, and generate content with AI. But 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 a complete analytical case study - 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.

Learn practical, hands-on methods to evaluate, measure, and improve AI products using data science to make better product decisions.

Principal Data Scientist, AI Evaluations

Principal Data Scientist, AI Evaluations at Ontra

This course equips you with the capability to measure, diagnose, and improve AI features in production. Specifically, it is the evolution of product data science and analytics for a world where AI product behavior is probabilistic, multi-step, and harder to observe. The focus is on measuring whether AI features actually deliver user value and business impact, and knowing what to do when they do not.If you already have product analytics foundations, the course shows how to adapt those foundations for AI systems. You do not need to be an AI specialist. Anyone can learn this. This however is not a foundations course about AI. You will get the most out of it if you are actively working on, or about to ship, AI features in a real product.-&gt; -&gt; -&gt; Join our <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">AI Builders Slack</a> community Work through AI eval design, learn Agentic Analytics workflows, and get feedback on what you’re building from other AI builders. <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">Click Here to Join Slack Community!</a>

Master the data-driven workflow behind reliable, high-quality AI products.

Make AI systems observable for product evaluation

Diagnose AI failures that block user success

Measure AI impact on user value and business outcomes

Operationalize evaluation across development, release, and production

Make better product decisions and drive AI product impact

You will get the most out of it if you are actively working on, or about to ship, AI features in a real product.

Real-World AI Product Experience

Product Managers: Shipping AI but can't prove impact or define good? Get a repeatable eval plan with metrics and decision rules.

Data Scientists and Analysts: Evals feel ad hoc and hard to trust? Build a system that links offline quality to user and business outcomes.

Engineers and ML Engineers: Evals live in notebooks and regressions slip? Operationalize with checks, monitoring, and tight feedback loops.

Design Experiments for AI Features

A/B tests are best for linking quality changes to user behavior

Where experiments fit in the AI impact chain

Learn eligibility, exposure, and unit of randomization so “treatment” means users actually experienced the AI.

Define exposure and randomization correctly

Interpret lift, variance, and segments well enough to decide ship, ramp, hold, or rollback with confidence.

Know when results are decision-grade

Learn Agentic Analytics workflows and AI Eval design. Get feedback on what you’re building from other AI builders.

Join AI Builders Slack Community. Link: bit.ly/ai-connect

Measure AI Impact with Causal Inference

Use it when you cannot randomize but still need decision-grade evidence for product or business impact.

Know when you need causal inference

Panel regression, diff-in-diff, and propensity matching: what each assumes and when each is appropriate.

Choose among three practical methods

Learn simple checks to spot confounding risk and communicate uncertainty without hand-waving.

Stress-test credibility before you trust it

Frame Questions That Drive Decisions with AI

Head of Data at Ontra, Ex-LinkedIn

Principal Data Scientist at Ontra

Senior Manager, Data Science 
@Superhuman (Prev. Grammarly)| Ex-Microsoft

Distinguish between questions that lead to actionable insights and questions that just generate busywork.

Identify questions that drive decisions over noise

Learn the 3-part framework that transforms fuzzy instincts into specific, decision-driving questions that AI answers.

Apply the framework transforming ideas to AI-ready analysis

Master the meta-workflow for using AI to refine questions, sanity-check outputs, and know when to trust AI.

Validate AI outputs using analytical judgment

Design Product Analysis with Claude Code

Head of Data at Ontra

Use Claude Code to formalize the decision, success metrics, guardrails, and “what changes our mind” thresholds.

Turn vague requests into decision-grade briefs

Generate a structured 1-pager: scope, cohorts, hypotheses, methods, outputs, plus risks and mitigations.

Build a full analysis design doc in Claude Code

Surface confounders, selection effects, baseline gaps, and scope creep. Add checks that reduce risk of failure.

Validate the plan in Claude Code before execution

Design Metrics That Don't Lie with AI

Head of Data at Ontra | Ex-Linkedin

Identify metrics that create action vs. those that just look good in reports

Distinguish vanity metrics from decision metrics

Transform vague success criteria into precise, measurable specifications that AI can operationalize with confidence

Apply the Metric Spec Framework

Master the workflow to define metrics with AI, sanity-check edge cases, and know when your metrics tell the truth

Validate metric quality with analytical judgment

Analyze Product Data in Claude Code

Analyze conversion or retention by segment, spot where drop-offs happen, and quantify where impact is coming from.

Run a deep dive analysis in Claude Code

Pressure-test the analysis with sanity checks and sensitivity cuts so conclusions hold up in reviews.

Validate results and catch common AI analysis errors

Translate the analysis into structured presentation of “what changed, why, who it affects, and what we should do next”.

Turn findings into a clear product recommendation

Design an AI Evaluation Plan

Decompose a real AI feature into inputs, context, outputs, UX, and outcomes, and list what to evaluate at each step.

Break an AI feature into evaluable steps

For each step, specify what evidence would justify ship, iterate, or stop, and what “evidence” is misleading.

Define evidence that supports a ship decision

Spot what’s missing in logging, annotation, or metrics, then capture it as clear next steps in the plan.

Identify gaps that block confident evaluation

Learn How to Run and Assess Experiments with AI

Senior DS Leader, Ex-Microsoft

Learn a practical framework to design experiments that are aligned to real decisions, not just statistical outcomes.

Running experiments with a clear, decision-focused framework

Use AI to review experiment setup, metrics, assumptions, and results to assess trustworthiness and actionability.

How to run experiments using a clear, decision-led framework

Learn how AI helps surface design flaws, metric issues, and interpretation risks, to know when to ship, iterate, or stop

Identify gaps and risks before acting
on experiments with AI

Crack Product Sense & Analytics Interviews with AI

Senior DS Leader (Ex-Microsoft)

Practice product sense and analytics questions with AI that pushes, probes, and mirrors real interviews.

Work with AI as an interview prep partner

Learn how to use AI to evaluate your responses across scoping, metrics, tradeoffs, and decisions

Assess your interview skills and pinpoint gaps

Structure answers consistently and use AI to test, refine, and improve them across repeated practice.

A clear framework for product sense & analytics interviews

Validate Business Impact of AI Features

Build a metric chain from AI output quality to a North Star proxy, then to a business metric (retention, conversion).

Connect AI quality to outcomes people care about

Learn 3 ways to test if quality moves the North Star: A/B tests, rollout comparisons, and directional evidence.

Tell whether your AI metric is decision-worthy

Turn the chain into a quick forecast so you can set rollout gates, prioritize work, and avoid over-optimizing.

Forecast AI feature impact before you invest months of work

Common reasons quality improves but the business does not, and how to diagnose where the link failed.

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