Quantitative Developer – Statistical Modeling & Analytics Location: Remote (Latin America) | Type: Full-Time About the Company We're a performance-driven digital marketing agency building proprietary software at the intersection of Google Ads, data analytics, and AI. Our platform turns raw advertising data into decision-grade signals across a portfolio of managed accounts. The product is live and actively used in production. This is early-stage product work, and we're expanding the team deliberately: hiring for complementary skill sets, not redundancy. About the Role We already have a strong engineer on the team with deep LLM, RAG, and systems-building experience. This hire is intentional and complementary: we're looking for someone whose center of gravity is quantitative, not software-first — a person who thinks in estimators, distributions, and model assumptions, and who can also ship that thinking as production code. You'll work directly with the founder and the existing engineer to own the analytical engine of the platform: the statistical logic that decomposes performance, detects real change from noise, and explains why metrics moved. Where the existing engineer builds the AI and evaluation layer, you build the quantitative foundation underneath it. This is not a pure data-science role — you'll write real product code in TypeScript and ship features in Next.js on a Supabase backend. But the reason we're hiring you is the math. Key Responsibilities Statistical Modeling & Analytics (the core of the role) * Design and implement the analytical methods behind the platform: log-space decomposition of performance metrics, rolling/windowed regression for elasticity and trend estimation, and change-point detection (e.g. CUSUM, PELT) to separate genuine shifts from noise. * Build anomaly detection that respects the realities of advertising data — non-stationarity, autocorrelation, fat tails — rather than assuming clean, normally distributed inputs. * Develop the quantitative logic for our "explain what changed" feature: quasi-experimental and counterfactual reasoning on observational data, where there is no clean control group and no ground truth. This is the hardest and most important part of the role. * Reconstruct joint distributions from marginal data (e.g…