Garrett Gignac · Data Science & Analytics Leader

I help companies figure out what actually drives growth.

I use experimentation, causal inference, product analytics, business judgment, and AI to turn ambiguous problems into evidence-backed decisions—and align teams around what to do next.

View selected work How I work

Start with the decision,
not the methodology.

I work best where the answer is not obvious, the data is imperfect, and the business needs more than another dashboard. I clarify what a team needs to learn, design credible measurement, and bring technical and business stakeholders together around what the evidence means and what comes next.

Selected work

Decisions, not deliverables.

Examples of how I connect analytical rigor to product, marketing, and growth decisions.

01 · UBER

Building causal measurement for B2B CRM

Problem Attribution could not show whether communications to employees at client companies actually changed behavior—while small samples, heterogeneity, and noisy outcomes made lift volatile.

Approach I helped lead an experimentation framework combining randomized holdouts, power analysis, regression/CUPED, principled trimming, and shrinkage where appropriate.

Impact Gave Data Science and Marketing a more credible, stable basis for deciding what to scale, change, or stop.

ExperimentationCausal inferenceCUPEDB2B measurement

Synthetic monthly effect estimates with confidence intervals

CUPED uses pre-treatment information to reduce unexplained variance and improve precision while targeting the same causal effect.

02 · TRIPADVISOR PLUS

Connecting supply to consumer product outcomes

Problem More participating hotels did not automatically mean a better traveler experience. The product only created value when relevant hotel searches could actually surface a compelling Plus offer.

Approach Measured Plus offer coverage across relevant hotel-shopping experiences and worked closely with the B2C Product Analytics team to connect supply availability to the consumer product experience.

Impact Gave the B2B team a product-oriented view of where Plus inventory was available—and where coverage gaps could limit the consumer proposition.

Product analyticsMarketplaceB2B2C

03 · AI-ENABLED PRODUCT

From campaign alert to AI-assisted diagnosis

Problem Monitoring surfaced underperformance, but diagnosing why remained fragmented and manually intensive.

Approach Used AI-assisted development to build a tool that also uses AI to investigate poor-performing campaigns using internal knowledge, campaign context, and performance data.

Impact Created a more direct path from anomaly to evidence-backed investigation while expanding what I could personally build.

AI-assisted developmentAI diagnosisAnalytics products

EXPERIMENTS & IDEAS

How should AI change experimentation?

Problem Teams waste scarce traffic testing weak hypotheses—and waste time rediscovering lessons from old experiments.

Approach AI can search prior work, sharpen hypotheses, screen variants, review design, and catch experiment-health problems. Live experiments still determine causal impact.

Potential impact More learning per test without lowering the standard of evidence.

Experimentation systemsAICausal rigor

How I think

A practical analytical loop.

The methodology serves the question. The analysis serves the decision.

Decision

What does the business need to decide?

Uncertainty

What do we need to learn?

Evidence

What would change our mind?

Design

What is the simplest credible approach?

Action

What changes because of the answer?

Causal measurement

Did X actually cause Y?

The model does not create causality. The credibility of the design determines how strongly the result can be interpreted.

Choose a condition to reveal the most credible next step.

Expertise

Problems I know how to solve.

Experimentation

A/B tests · holdouts · power · guardrails · heterogeneous effects · incrementality · CUPED · variance reduction

Causal inference

Counterfactuals · quasi-experiments · confounding · difference-in-differences · matching

Product analytics

Funnels · adoption · retention · segmentation · subscriptions · marketplaces

Marketing science

CRM · attribution · paid media · campaign analytics · LTV/CAC

Business analytics

Unit economics · KPIs · forecasting · strategic analysis · investment decisions

AI-enabled analytics

Rapid prototyping · analytical products · workflows · decision-support tools

About

Technical rigor. Business judgment.

I have spent my career helping teams answer difficult questions with data—not simply what happened, but why it happened, what actually caused it, and what the business should do next.

I am particularly effective when the question is ambiguous, the stakes are real, multiple stakeholders are involved, and the analytical path is not obvious. I have led analysts and data scientists, partnered across Product, Marketing, and business teams, and translated complex evidence for senior leaders. My value is not a single method. It is framing the right problem, choosing the right level of rigor, and aligning people around action.

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Working on a hard data problem?

I'm interested in senior Data Science, Analytics, Decision Science, and Product Analytics opportunities.

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Examples are generalized from prior work. Any data shown is illustrative or synthetic, and no confidential or proprietary information is included.