ph3PURPOSE OF THE ROLE /h3 pThe Integral Data Scientist will be the technical leader responsible for developing advanced analytical models and machine learning systems that optimize pricing decisions and promotional strategies across 5,500+ locations with millions of SKUs globally. This hands‑on technical leadership role requires you to spend 60‑70% of your time building models, writing production‑quality code, and architecting ML systems, with the remaining time on technical mentorship and translating complex models into actionable insights for commercial teams. You will work from Madrid or Milan with hybrid flexibility. /p h3RESPONSIBILITIES /h3 h3Advanced Modeling Algorithm Development /h3 ul liDevelop price elasticity models using econometric techniques (regression, mixed effects models, instrumental variables) to estimate demand curves at SKU‑category‑location levels /li liBuild dynamic pricing algorithms that optimize prices in near‑real‑time based on competitor actions, demand signals, inventory levels, and strategic constraints /li liCreate price architecture frameworks (zones, tiers, good‑better‑best) using clustering, segmentation, and optimization techniques /li liDesign margin optimization models that balance volume and profitability trade‑offs /li liBuild causal inference models to measure true incrementality of promotions, accounting for cannibalization and pull‑forward effects /li liDevelop promotion ROI prediction models that recommend optimal mechanics (% discount, BOGO, bundles), timing, and target segments /li liCreate promotion planning optimization algorithms that maximize ROI under budget constraints while avoiding overlap /li liImplement models using Python (pandas, scikit‑learn, statsmodels, PyMC3, XGBoost) with production‑quality code /li liBuild robust data pipelines (Airflow, Spark) for pricing, sales, competitor, and promotional data at scale /li liDeploy models to production (AWS/GCP/Azure) with proper monitoring, alerting, and automated retraining workflows /li /ul h3Technical Leadership Collaboration /h3 ul liSet technical standards for data science work: code quality, testing, documentation, peer review processes /li liConduct thorough code reviews for other data scientists, providing constructive feedback and ensuring quality /li liMentor mid‑level data scientists on modeling techniques, coding best practices,



and business acumen /li liArchitect ML system design for pricing/promo products in collaboration with BI engineering teams /li liCollaborate with Principal TPM on product roadmap, translating business requirements into technical approaches /li liStay current with state‑of‑the‑art research in pricing/revenue optimization, econometrics, and causal inference /li liContribute to technical hiring by conducting data science interviews and assessing candidate depth /li /ul h3Business Partnership Communication /h3 ul liTranslate complex model outputs into clear, actionable insights for commercial teams (category managers, regional pricing leads) /li liPresent model results and recommendations to C‑suite executives (CCO, CFO, regional heads) in accessible terms /li liDesign and analyze A/B tests and quasi‑experiments to validate models and measure business impact in production /li liPartner with regional teams to understand local market dynamics and competitive landscapes that inform models /li liBuild trust with stakeholders by demonstrating models reflect real‑world dynamics and deliver tangible value /li liCreate compelling data visualizations and dashboards (Tableau, Power BI, Python) that communicate insights effectively /li liDevelop training materials and workshops to upskill commercial teams on data‑driven pricing and promotion concepts /li /ul h3WHAT WE ARE LOOKING FOR /h3 h3Education Technical Foundation /h3 ul liMS or PhD in a quantitative field (Computer Science, Statistics, Economics, Operations Research, Applied Mathematics, Physics, Engineering) OR Bachelor’s degree with 8+ years of applied data science experience demonstrating equivalent depth /li liExpert‑level Python proficiency for data science (pandas, numpy, scikit‑learn, statsmodels, scipy) with clean, production‑quality coding /li liAdvanced SQL skills – complex queries (CTEs, window functions, optimization) on large datasets (100M+ rows) /li liStrong foundation in statistics and econometrics: regression, hypothesis testing, causal inference, time series /li /ul h3Pricing Revenue Optimization Expertise /h3 ul li6+ years of applied data science experience with at least 3+ years in pricing, revenue management, yield optimization,



or dynamic pricing /li liDeep understanding of pricing theory: demand elasticity, price discrimination, competitive game theory, psychological pricing /li liHands‑on experience building and deploying price optimization models in production environments /li liProven track record of models driving measurable business impact (€/$ millions in revenue or margin improvement) /li liExperience in retail, e‑commerce, travel, hospitality, or marketplace businesses strongly preferred /li /ul h3Machine Learning Advanced Analytics /h3 ul liStrong ML fundamentals: supervised learning (regression, tree‑based, ensembles), unsupervised learning (clustering, dimensionality reduction) /li liExperience with causal inference techniques (diff‑in‑diff, synthetic controls, instrumental variables, propensity score matching) /li liProficiency with experimentation: A/B test design, power analysis, sequential testing, multiple hypothesis correction /li liFamiliarity with optimization algorithms (linear programming, constraint satisfaction, dynamic programming) /li liTrack record of deploying ML models to production with monitoring, retraining, and alerting (not just Jupyter notebooks) /li liExperience with cloud platforms (AWS, GCP, Azure) and ML infrastructure (model serving, feature stores, orchestration) /li liUnderstanding of MLOps best practices: versioning, reproducibility, CI/CD for ML, data quality monitoring /li liExcellent written and verbal communication in English – able to explain complex technical concepts to non‑technical audiences /li liExperience presenting to senior executives (C‑suite level) with data‑driven recommendations /li liProven ability to collaborate with cross‑functional teams (product, engineering, business stakeholders) /li liStrong business acumen – understands PL dynamics, commercial trade‑offs, and ROI calculations /li /ul h3Preferred Qualifications /h3 ul liRole can be based in Spain (Madrid) or Italy (Milan) /li liPhD in Economics, Operations Research, or Statistics with focus on pricing/auctions/mechanism design /li li8+ years data science experience with progression to lead/principal level /li liExperience at top‑tier tech companies or high‑growth startups would be a plus /li liPrevious work on large‑scale pricing/revenue systems (billions in GMV/revenue influenced) /li liExperience with reinforcement learning for pricing or promotion optimization /li /ul /p #J-18808-Ljbffr

📌 Global Data Scientist (Madrid)
🏢 Avolta
📍 Madrid