INTERNSHIP : Data Science (Python & ML)

We’re hiring a

Python‑first, statistics‑strong

Data Science Intern (or junior ML Engineer) to partner with our

Data Analysts

on the hardest problems we have :

probabilistic LTV

churn prediction

, and

forecasting

that actually moves CAC, payback, and ROAS.

⚠️ READ THIS FIRST — Apply only if you meet this :

Eligibility : You’re currently studying at a university in Spain (ideally in or near Barcelona) and can sign a "convenio de prácticas". This position is open exclusively for internship agreements.

If you’re not sure whether your university can provide one,

please confirm with them before applying

, as we’ll

automatically filter out candidates who can’t meet this requirement.

Otherwise, feel free to check out our other

junior roles

on the Carrots Lab LinkedIn page › Jobs

where a "convenio" isn’t required.

⭐ WHAT YOU’LL OWN (Your Mission)

LTV Prediction & Probabilistic Forecasts

Build, compare, and iterate LTV models (e.g., survival / retention‑based, zero‑inflated + Gamma, GBMs). Produce

calibrated

predictions we can trust for UA and pricing.

Churn & Retention Modeling

Predict churn windows and retention curves by app / geo / cohort to inform payback and creative / keyword strategy.

Signal Engineering for UA

Translate model outputs into

conversion signals

(e.g., value‑based bidding targets) that improve Google / Meta optimization.

Revenue & Cohort Analytics (Python)

Pull, transform, and join event & purchase data (RevenueCat, AppsFlyer, Firebase, BigQuery) into model‑ready datasets.

Decision Science

Evaluate A / B tests (frequentist or Bayesian), pricing experiments, and funnel changes with

confidence intervals

and

practical uplift

WHAT SUCCESS LOOKS LIKE (90 Days)

LTV v1 shipped

per flagship app with baseline vs. model comparison (e.g., MAE / RMSE or calibration plots) and a documented retrain cadence.

Churn or retention model v1

that beats naive benchmarks (e.g., 30‑day retention mean) and informs UA payback assumptions.

Signal spec

delivered : which in‑app events / values we should pass to ad platforms, how to weight them, expected CPA impact.

Reproducible codebase

: clean notebooks / scripts with README, functions, and basic tests; data pulls automated in Python.

Live dashboards

(with Sergi) : LTV / payback & cohort views wired to

Looker

for weekly decision‑making.