Global Data Scientist

Avolta is the world’s leading travel experience player. 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 2027 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.

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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. Your work will directly influence billions in revenue and millions in margin improvement. This is a hands‑on technical leadership role where you will 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. Success in this position depends on deep technical expertise in pricing/revenue optimization, ability to ship production ML systems at scale, and effectiveness in communicating with non‑technical business stakeholders. This role will be based either in Madrid or Milan with hybrid flexibility.

RESPONSIBILITIES
Advanced Modeling & Algorithm Development

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• 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
  

Technical Leadership & Collaboratiion

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• 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
  

Business Partnership & Communication

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• 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 an