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Internship: Data Science (Python & Ml) Job Opening In Barcelona – Now Hiring carrots


Job description

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 inPython.

  • Live dashboards (with Sergi): LTV/payback & cohort views wired to Looker for weekly decision‑making.


Required Skill Profession

Science


  • Job Details

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Unlock Your Internship Data Potential: Insight & Career Growth Guide


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3. Impartiapty

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