Data Analysis Scientist (Permanent)

With a traveler-centric philosophy and a geographically diverse network, the travel retail and F&B company addresses the needs of up to 2.3 billion passengers each year, with 5,500 outlets in more than 75 countries across six continents.Guided by their Destination ** strategy and boosted by their recent combination with travel F&B giant Autogrill, the company is well positioned to realize their ambition to create a Travel Experience Revolution through their many locations at airports, motorways, cruise lines, seaports and railway stations amongst others.PURPOSE OF THE ROLE 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 is a hands?on technical leadership role where you will spend ***% 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.This role will be based either in Madrid or Milan with hybrid flexibility .Build dynamic pricing algorithms that optimize prices in near-real-time based on competitor actions, demand signals, inventory levels, and strategic constraints 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 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) 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 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 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 ~ Machine Learning & Advanced Analytics Experience with causal inference techniques (diff-in-diff, synthetic controls, instrumental variables, propensity score matching) A/B test design, power analysis, sequential testing, multiple hypothesis correction 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 Strong business acumen - understands P&L dynamics, commercial trade-offs, and ROI calculations 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 ~

engineering teams 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) 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 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 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 ~ Machine Learning & Advanced Analytics Experience with causal inference techniques (diff-in-diff, synthetic controls, instrumental variables, propensity score matching) A/B test design, power analysis, sequential testing, multiple hypothesis correction 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 Strong business acumen - understands P&L dynamics, commercial trade-offs, and ROI calculations 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 ~

Madrid, España

With a traveler-centric philosophy and a geographically diverse network, the travel retail and F&B company addresses the needs of up to 2.3 billion passengers each year, with 5,500 outlets in more than 75 countries across six continents. Guided by their Destination ** strategy and boosted by their recent combination with travel F&B giant Autogrill, the company is well positioned to realize their ambition to create a Travel Experience Revolution through their many locations at airports, motorways, cruise lines, seaports and railway stations amongst others. PURPOSE OF THE ROLE 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 is a hands?on technical leadership role where you will spend ***% 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. This role will be based either in Madrid or Milan with hybrid flexibility .