Global Data Scientist

The Global 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. RESPONSIBILITIES Advanced Modeling & Algorithm Development

• Develop price elasticity models using econometric techniques (regression, mixed effects models, instrumental variables) to estimate demand curves at SKU‑category‑location levels
• Build dynamic pricing algorithms that optimize prices in near‑real‑time based on competitor actions, demand signals, inventory levels, and strategic constraints
• Create price architecture frameworks (zones, tiers, good‑better‑best) using clustering, segmentation, and optimization techniques
• Design margin optimization models that balance volume and profitability trade‑offs
• Build causal inference models to measure true incrementality of promotions, accounting for cannibalization and pull‑forward effects
• Develop promotion ROI prediction models that recommend optimal mechanics (% discount, BOGO, bundles), timing, and target segments
• Create promotion planning optimization algorithms that maximize ROI under budget constraints while avoiding overlap
• Implement models using Python (pandas, scikit‑learn, statsmodels, PyMC3, XGBoost) with production‑quality code
• Build robust data pipelines (Airflow, Spark) for pricing, sales, competitor, and promotional data at scale
• Deploy models to production (AWS/GCP/Azure) with proper monitoring, alerting, and automated retraining workflows

• Set technical standards for data science work: code quality, testing, documentation, peer review processes
• Conduct thorough code reviews for other data scientists, providing constructive feedback and ensuring quality
• Mentor mid‑level data scientists on modeling techniques, coding best practices, and business acumen
• Architect ML system design for pricing/promo products in collaboration with BI engineering teams
• Collaborate with Principal TPM on product roadmap, translating business requirements into technical approaches
• Stay current with state‑of‑the‑art research in pricing/revenue optimization, econometrics, and causal inference
• Contribute to technical hiring by conducting data science interviews and assessing candidate depth

• Translate complex model outputs into clear, actionable insights for commercial teams (category managers, regional pricing leads)
• Present model results and recommendations to C‑suite executives (CCO, CFO, regional heads) in accessible terms
• Design and analyze A/B tests and quasi‑experiments to validate models and measure business impact in production
• Partner with regional teams to understand local market dynamics and competitive landscapes that inform models
• Build trust with stakeholders by demonstrating models reflect real‑world dynamics and deliver tangible value
• Create compelling data visualizations and dashboards (Tableau, Power BI, Python) that communicate insights effectively
• Develop training materials and workshops to upskill commercial teams on data‑driven pricing and promotion concepts

• MS 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
• Expert‑level Python proficiency for data science (pandas, numpy, scikit‑learn, statsmodels, scipy) with clean, production‑quality coding
• Advanced SQL skills – complex queries (CTEs, window functions, optimization) on large datasets (100M+ rows)
• Strong foundation in statistics and econometrics: regression, hypothesis testing, causal inference, time series

• 6+ years of applied data science experience with at least 3+ years in pricing, revenue management, yield optimization, or dynamic pricing
• Deep understanding of pricing theory: demand elasticity, price discrimination, competitive game theory, psychological pricing
• Hands‑on experience building and deploying price optimization models in production environments
• Proven track record of models driving measurable business impact (€/$ millions in revenue or margin improvement)
• Experience in retail, e‑commerce, travel, hospitality, or marketplace businesses strongly preferred

• Strong ML fundamentals: supervised learning (regression, tree‑based, ensembles), unsupervised learning (clustering, dimensionality reduction)
• Experience with causal inference techniques (diff‑in‑diff, synthetic controls, instrumental variables, propensity score matching)
• Proficiency with experimentation: A/B test design, power analysis, sequential testing, multiple hypothesis correction
• Familiarity with optimization algorithms (linear programming, constraint satisfaction, dynamic programming)
• Track record of deploying ML models to production with monitoring, retraining, and alerting (not just Jupyter notebooks)
• Experience with cloud platforms (AWS, GCP, Azure) and ML infrastructure (model serving, feature stores, orchestration)
• Understanding of MLOps best practices: versioning, reproducibility, CI/CD for ML, data quality monitoring
• Excellent written and verbal communication in English – able to explain complex technical concepts to non‑technical audiences
• Experience presenting to senior executives (C‑suite level) with data‑driven recommendations
• Proven ability to collaborate with cross‑functional teams (product, engineering, business stakeholders)
• Strong business acumen – understands P&L dynamics, commercial trade‑offs, and ROI calculations

• Role can be based in Spain (Madrid) or Italy (Milan)
• PhD in Economics, Operations Research, or Statistics with focus on pricing/auctions/mechanism design
• 8+ years data science experience with progression to lead/principal level
• Experience at top‑tier tech companies or high‑growth startups would be a plus
• Previous work on large‑scale pricing/revenue systems (billions in GMV/revenue influenced)
• Experience with reinforcement learning for pricing or promotion optimization