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    "result": {"pageContext":{"slug":"/senior-data-science-consultant-with-conversational-spanish/","title":"Senior Data Science Consultant (with conversational Spanish)","html":"<p><strong>Role Type:</strong> Contract / Fixed-Scope Consulting Engagement<br>\n<strong>Duration:</strong> 2–3 months (10–12 weeks)<br>\n<strong>Allocation:</strong> Full-time or near full-time (4–5 days/week preferred)<br>\n<strong>Location:</strong> Remote (Must overlap with Spain / CET ± 6 hours)<br>\n<strong>Languages Required:</strong> Spanish (C1+) and English (B2+)</p>\n<p><strong>The Opportunity</strong></p>\n<p>We are looking for a hands-on <strong>Senior Data Science / Analytics Engineering Consultant</strong> to audit, redesign, and rebuild our core <strong>chargeback estimation</strong> and <strong>revenue estimation</strong> models.</p>\n<p>This is a critical, end-to-end project. You will turn existing prototypes into production-grade pipelines within our modern data stack (<strong>dbt on Databricks &#x26; Python</strong>). Additionally, you will design and build a lightweight, business-facing scenario tool that enables finance and operations stakeholders to run “what-if” revenue simulations.</p>\n<p><strong>Our Technical Philosophy: Parsimony Over Black Boxes</strong></p>\n<p>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, <strong>this is not the project for you</strong>.</p>\n<p>We build models where every parameter carries an explicit business meaning that can be explained to non-technical stakeholders in plain language (e.g., <em>“If price increases by 10%, conversion drops by X% because our calculated price elasticity is −1.3”</em>).</p>\n<p><strong>Scope of Work &#x26; Deliverables</strong></p>\n<ul>\n<li><strong>Phase 1: Discovery &#x26; Audit (Weeks 1–2):</strong> 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.</li>\n<li><strong>Phase 2: Redesign (Weeks 3–4):</strong> Propose the target parametric methodology for each model (granularity, refresh cadence, uncertainty quantification) and secure stakeholder sign-off.</li>\n<li><strong>Phase 3: Rebuild (Weeks 5–9):</strong> 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.</li>\n<li><strong>Phase 4: Scenario Tool (Weeks 9–10):</strong> 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.</li>\n<li><strong>Phase 5: Handover (Weeks 11–12):</strong> Conduct knowledge transfer sessions, deliver runbooks/model cards, and provide a 2-week post-handover bug-fix support window.</li>\n</ul>\n<p><strong>Your Profile</strong></p>\n<ul>\n<li><strong>Experience:</strong> 5–8+ years in Data Science, Quant Analytics, or Senior Analytics Engineering.</li>\n<li><strong>Production Ownership:</strong> Proven track record of owning financial-estimation or forecasting models in production end-to-end.</li>\n<li><strong>The “Parsimony” Mindset:</strong> Demonstrable expertise in parametric/curve-based modeling (price elasticity, cohort survival curves, demand curves, GLMs) over black-box ML.</li>\n<li><strong>Stakeholder Tooling:</strong> Experience building clean “what-if” tools or financial planning interfaces for business leadership.</li>\n<li><strong>The Stack:</strong> Advanced Python (<code>pandas</code>, <code>numpy</code>, <code>scikit-learn</code>, <code>statsmodels</code>, and/or <code>scipy.optimize</code>), production <code>dbt</code> (incremental models, snapshots, tests), and <code>Databricks</code> (<code>PySpark</code>, <code>Delta</code>, workflows).</li>\n<li><strong>Domain Knowledge:</strong> Direct experience in payment risk, chargebacks, refund/dispute modeling, revenue forecasting, cohort LTV, or pricing analytics.</li>\n<li><strong>Communication:</strong> Exceptional ability to cross-reference data science outputs with strict financial accounting figures.</li>\n<li><strong>Languages:</strong> <strong>Spanish (C1+)</strong> is mandatory for daily synchronization with local finance/ops teams; <strong>English (B2+)</strong> is required for technical documentation.</li>\n</ul>\n<p><strong>Nice to Have</strong></p>\n<ul>\n<li>Background in FinTech, subscription models, payments, or high-volume e-commerce.</li>\n<li>Deep familiarity with Unity Catalog, Databricks Workflows, or Airflow.</li>\n<li>A strong track record of successful independent consulting engagements (references requested).</li>\n</ul>","location":"Ukraine/Europe"}},
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