ECOM IGNITE is a growth platform for D2C brands (ecomignite.ai) for ad performance tracking, built on Supabase (Edge Functions + Postgres). Beyond basic trend forecasting, we're building a causal measurement layer — separating true incremental demand from captured demand, and identifying spend efficiency ceilings per channel/account. This is a deeper, methodology-driven build than a standard forecasting feature, closer to what tools like MMM vendors and analytics platforms offer their enterprise clients.
What You'll Build
A causal / Marketing Mix Model (MMM) that separates demand generation, demand capture, and baseline (organic) revenue — controlling for seasonality and confounders
A spend-efficiency-ceiling model: saturation/response curves per channel or account showing where marginal ROAS drops below a usable threshold
A defensible accuracy framework — in-sample and out-of-sample error reporting with confidence intervals, not a single headline accuracy number
Documentation explaining model assumptions and structure in plain language, so the methodology can be defended to clients




Our Data Reality (read before applying)
Accounts vary wildly in volume — some run high monthly spend with rich daily data, others are much smaller and sparser. The model needs to handle both without breaking or producing unstable estimates on thin data.
Data arrives via Meta/Google Ads APIs and can have gaps, expired references, and inconsistent formatting.
We're not trying to build a research-paper-grade model — we need something rigorous enough to trust and defend, but pragmatic enough to ship and maintain.
Requirements
Strong background in causal inference or econometrics (difference-in-differences, instrumental variables, or Bayesian causal graphs) applied to marketing/business data
Hands-on MMM experience — ideally with Robyn, Google Meridian, LightweightMMM, or a custom Bayesian implementation (PyMC / Stan)
Robust Python (pandas, statsmodels, PyMC or equivalent probabilistic progra

📌 Freelance Data Science (India)
🏢 eCom Ignite
📍 India