Job VC

Role Type:
Contract / Fixed-Scope Consulting Engagement
Duration:
2–3 months (10–12 weeks)
Allocation:
Full-time or near full-time (4–5 days/week preferred)
Location:
Remote (Must overlap with Spain / CET ± 6 hours)
Languages Required:
Spanish (C1+) and English (B2+)

The Opportunity
We are looking for a hands-on
Senior Data Science / Analytics Engineering Consultant
to audit, redesign, and rebuild our core
chargeback estimation
and
revenue estimation
models.
This is a critical, end-to-end project. You will turn existing prototypes into production-grade pipelines within our modern data stack (
dbt on Databricks & Python
). Additionally, you will design and build a lightweight, business-facing scenario tool that enables finance and operations stakeholders to run “what-if” revenue simulations.
Our Technical Philosophy: Parsimony Over Black Boxes
We explicitly favor simple, highly interpretable, parametric approaches. If your first instinct for a forecasting problem is to throw an XGBoost ensemble, a deep learning model, or an LLM at it,
this is not the project for you
.
We build models where every parameter carries an explicit business meaning that can be explained to non-technical stakeholders in plain language (e.g.,
“If price increases by 10%, conversion drops by X% because our calculated price elasticity is −1.3”
).

Scope of Work & Deliverables
Phase 1: Discovery & Audit (Weeks 1–2):
Review current estimation models (assumptions, dbt lineage, validation methods). Reconcile historical model outputs against actual accounting figures for the last 12–24 months to quantify bias and error. Deliver a written findings report.
Phase 2: Redesign (Weeks 3–4):
Propose the target parametric methodology for each model (granularity, refresh cadence, uncertainty quantification) and secure stakeholder sign-off.
Phase 3: Rebuild (Weeks 5–9):
Productionize the new models in dbt, Databricks, and Python. Use curve-based formulations with fitted historical parameters and clear confidence intervals. Implement strict unit/dbt testing and MLflow tracking.
Phase 4: Scenario Tool (Weeks 9–10):
Build a lightweight business interface (e.g., Streamlit on Databricks or a parameterized Databricks SQL dashboard). Users must be able to input hypotheses (price ±X%, volume ±Y%, mix shifts) and instantly view projected net impact with uncertainty bands.
Phase 5: Handover (Weeks 11–12):
Conduct knowledge transfer sessions, deliver runbooks/model cards, and provide a 2-week post-handover bug-fix support window.

Your Profile
Experience:
5–8+ years in Data Science, Quant Analytics, or Senior Analytics Engineering.
Production Ownership:
Proven track record of owning financial-estimation or forecasting models in production end-to-end.
The “Parsimony” Mindset:
Demonstrable expertise in parametric/curve-based modeling (price elasticity, cohort survival curves, demand curves, GLMs) over black-box ML.
Stakeholder Tooling:
Experience building clean “what-if” tools or financial planning interfaces for business leadership.
The Stack:
Advanced Python (pandas, numpy, scikit-learn, statsmodels, and/or scipy.optimize), production dbt (incremental models, snapshots, tests), and Databricks (PySpark, Delta, workflows).
Domain Knowledge:
Direct experience in payment risk, chargebacks, refund/dispute modeling, revenue forecasting, cohort LTV, or pricing analytics.
Communication:
Exceptional ability to cross-reference data science outputs with strict financial accounting figures.
Languages:

Spanish (C1+)
is mandatory for daily synchronization with local finance/ops teams;
English (B2+)
is required for technical documentation.

Nice to Have
Background in FinTech, subscription models, payments, or high-volume e-commerce.
Deep familiarity with Unity Catalog, Databricks Workflows, or Airflow.
A strong track record of successful independent consulting engagements (references requested).