Job title: Senior Data Scientist
Location: Remote (Within Spain)
Contract Type: Freelance/Contract
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We're looking for an experienced Data Scientist to audit, redesign and rebuild chargeback estimation and revenue forecasting models. You will work on production grade pipelines within a dbt on Databricks stack, alongside a business facing scenario tool that which stakeholders to model the impact of price changes on expected revenue
Key Responsibilities
* Audit and redesign chargeback estimation and revenue forecasting models
* Deliver production-grade pipelines in dbt (Databricks) and Python
* Build scenario/what-if tools for non-technical stakeholders (price sensitivity, revenue planning, unit economics)
* Apply interpretable, parametric modelling approaches — closed-form equations, demand/price curves, elasticity models, cohort survival curves, GLMs, and additive decompositions
* Ensure every model parameter carries a clear business meaning explainable in plain language
* Reconcile model outputs against accounting figures and communicate uncertainty in business terms
Required Experience
* 5–8 years of senior data science or analytics engineering experience
* End-to-end ownership of a forecasting or financial estimation model in production
* Strong bias toward parsimony: price elasticity, demand curves, cohort/LTV curves, survival models, GLMs preferred over black-box ML
* Stack: Python (pandas, numpy, scikit-learn, statsmodels, scipy.Optimize), dbt (incremental models, tests, snapshots), Databricks (PySpark, Delta, jobs), advanced SQL
* Domain: Demonstrated work on chargebacks, payment risk, refund/dispute modelling, xhfqzwm revenue forecasting, LTV, cohort revenue projections, or pricing/elasticity
* Languages: Spanish C1+ (working day-to-day with finance/ops stakeholders) and English B2+ (documentation)
* Comfortable reconciling model outputs against accounting figures and explaining uncertainty in business terms
Nice to Have
* Payments, fintech, subscription, or e-commerce background
* MLflow on Databricks, Unity Catalog, Databricks Workflows, or Airflow
* Prior consulting or fixed-scope engagement track record (references preferred)