Operator in Residence

Triple Whale

10d

About The Position

As a Measurement Strategist on Triple Whale's Marketing Science team, you'll be the operator voice inside the Compass and Unified Measurement customer experience. This role exists because brands at scale ($50M to $500M+ in revenue, running complex omnichannel operations) don't just need data. They need someone who has been in their seat and knows how to translate measurement outputs into decisions that can actually survive contact with a real organization. You'll work as part of a two-person service duo alongside a Marketing Data Scientist, who owns model configuration and statistical rigor for MMM and incrementality tests. Your lane is the other half: operator translation, strategic advisory, and the measurement roadmap that ties it all together for the customer. Together, you cover the full conversation from model to decision. You'll own the measurement strategy conversation for a portfolio of Compass brands: from kickoff through incrementality test design, MMM interpretation, attribution gap analysis, and the sequenced roadmap that follows. You'll also be a named resource for sales when a high-value prospect needs operator credibility in the room, not a product pitch, but someone who genuinely understands what they're dealing with. The role sits on the Marketing Science team and works closely with Product, Data Science, and CSM. You're the connective tissue between the complexity brands live in and the tools Triple Whale builds to address it.

Requirements

Real operator experience at scale You've been accountable for marketing performance, not as an agency buyer or advisor, but as someone with skin in the game at a brand doing meaningful revenue. You've made budget calls with imperfect data, defended channel spend to a CFO, and felt the organizational complexity that makes good measurement advice hard to act on.
Measurement literacy You understand the difference between last-click, MTA, MMM, and incrementality testing, and which one answers which question. You can explain test confidence, null results, and attribution gaps in plain language. You don't need to build the models; you need to make them mean something to an operator who has to act on them.
Paid media and channel expertise 5+ years running or closely overseeing paid media across Meta, Google, TikTok, or similar platforms at meaningful scale. You understand platform mechanics, attribution windows, and reporting gaps, and you have formed, articulable opinions about how each platform works and what it systematically gets wrong.
Omnichannel fluency You understand what it means to run marketing across DTC, Amazon, wholesale, and retail simultaneously, and why the attribution problems that creates are genuinely hard. You don't assume digital-only measurement frameworks apply cleanly to brands with offline complexity.
Executive-level communication You can run a high-stakes call with a CFO, CMO, and media buyer all in the room, adjusting depth and framing for each without losing any of them. You can deliver hard news (a null result, a recommendation that contradicts their instincts) without losing the relationship.

Nice To Haves

Direct experience at or with a brand doing $50M–$500M+ in revenue, especially with retail, wholesale, or Amazon complexity on top of DTC
Hands-on familiarity with Triple Whale, Northbeam, Rockerbox, or similar multi-platform attribution tools, with genuine opinions about their tradeoffs
Experience evaluating or acting on incrementality test results (GeoLift, holdout, synthetic control) in a context where the findings informed a real budget decision
Comfort operating where the playbook is being built alongside the delivery

Responsibilities

Lead measurement strategy for Compass brands
Own the full measurement conversation arc, from kickoff and test design through results interpretation and what-comes-next recommendations, for a portfolio of 8–9 figure brands
Translate MMM, incrementality, and attribution model outputs into operator-native decisions: not "here's what the model says," but "here's what it means for your channel mix and your next budget call"
Sequence tests intelligently across channels based on the brand's spend levels, organizational capacity, and the questions that actually matter to their business right now
Interpret results for non-technical stakeholders: explain what a null result means, what 80% confidence does and doesn't authorize, and how an Amazon halo effect finding changes cross-channel thinking
Help brands build a measurement roadmap over time, not just run one-off tests
Be the channel expert in the room
Bring working knowledge of the channels these brands actually run, including Meta, Google, TikTok, YouTube, retail media, and AppLovin, with a clear understanding of what each platform reports, what it doesn't, and where platform-reported performance diverges from business reality
Help brands with offline complexity (wholesale, retail, Amazon) think through measurement in contexts where digital-only frameworks don't cleanly apply
Reframe the questions brands bring: not "is my Meta ROAS good?" but "are these campaigns driving incremental new customers, or harvesting the brand awareness I've already built?"
Apply cross-brand pattern intelligence, without violating confidentiality, to contextualize what individual brands are seeing in their data
Support sales as a subject matter expert
Join high-value prospect conversations as the operator credibility voice, not to pitch product, but to make a $200M omnichannel brand feel genuinely understood before a deal decision gets made
Ask the questions that open the conversation: the ones that show you understand what it actually feels like to run marketing at their scale, even before Triple Whale is fully on the table
Translate Triple Whale's capabilities into their operational reality, not into feature descriptions
Feed the product from the outside in
Distinguish between product gaps and operator context gaps: not everything that confuses a brand is a product problem, and knowing the difference is what makes your feedback actually useful
Name patterns when multiple brands at similar scale hit the same friction point, and bring them to the product team with enough context to inform prioritization
Bring the organizational reality that product can't get from the inside: what does it feel like to act on an MMM recommendation when there are 14 stakeholders with opinions on the campaign structure?