Clarity before velocity
Name the decision, evidence standard, owner, and next move before increasing output.
I build growth systems that help leaders decide what to fund, what to stop, and what the evidence actually proves. My work spans in-house enterprise programs, founder-led products, and applied research.
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I’m a growth experimentation leader based in Austin, Texas. I use behavioral economics to form stronger hypotheses, statistical discipline to protect the decision, and business context to connect the result to revenue.
The through-line is not a tool or channel. It is building decision systems that make learning faster and investment more defensible.
Built the operating system for a multi-brand enterprise experimentation program: intake, prioritization, statistical rules, QA, and revenue reporting.
Connected acquisition, product usage, CRM, and funded-account outcomes to support growth investment and market expansion decisions.
Builds software and research projects under the same constraint as any founder: learning speed, limited resources, and direct accountability for outcomes.
Name the decision, evidence standard, owner, and next move before increasing output.
Use the strongest causal method the business can support, then explain the limits plainly.
Document standards and learning so the organization improves when one person leaves the room.
Built and grew products with organic acquisition, rapid learning loops, and direct accountability for delivery.
Explore the venture portfolio →The full inventories live in focused hubs where they are easier to scan and maintain.
View the working stack → Browse the reading shelf →Every article reflects Atticus’s own research, experiments, and behavioral economics interpretation. AI may assist with research synthesis and prose drafting; it does not replace the source material, methodology judgment, or editorial accountability.
Employer-sensitive experiment data is anonymized and expressed as safe ranges. Full-AI generation is not presented as Atticus’s first-hand work.