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.

PURPOSE OF THE ROLE The 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. 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 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 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 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

WHAT WE ARE LOOKING FOR Education &

- Technical Foundation 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

Pricing &

- Revenue Optimization Experties 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

Machine Learning &

- Advanced Analytics 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

Preferred Qualifications 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

📌 Global Data Scientist (Madrid)
🏢 Avolta
📍 Madrid