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 **** 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 Senior Analyst will be the data engine powering strategic decisions and operational excellence for Avolta's Commercial Optimization team.
Every week, commercial leaders will rely on your analyses to understand business performance, identify opportunities, and make critical decisions across pricing, assortment, promotions, and inventory optimization.
You will answer urgent questions from the CCO, uncover hidden insights in data, and ensure our data science models are grounded in business reality.
This is a high-impact, high-visibility role requiring exceptional analytical horsepower, business acumen, speed of execution, and communication skills.
You must be comfortable diving deep into data one moment and presenting findings to C-suite executives the next.
Success depends on technical strength (SQL expert, analytical wizard), business savvy (understands commercial dynamics), and clear communication ability to tell compelling stories with data.
RESPONSIBILITIES
Recurring Business Analytics & Reporting
Own weekly commercial performance reporting: analyze sales trends, margin evolution, pricing actions, competitive moves, promotional performance across categories, regions, and locations
Prepare concise executive dashboards and commentary for CCO, CFO, and commercial leadership highlighting key trends, anomalies, and actionable insights
Conduct deep-dives on performance variances: investigate why sales/margin are up or down, attributing changes to pricing, volume, mix, competition, or external factors
Monitor KPIs for all four commercial optimization pillars (pricing, assortment, promotions, inventory) and flag issues proactively
Respond rapidly (within hours to 1-2 days) to urgent analytical questions from CCO, CFO, regional heads, or commercial leadership
Analyze competitor pricing data (collected via scraping) to identify competitive gaps, opportunities, and threats
Produce regular competitive benchmarking reports: Avolta vs. key competitors by category, location, and pricing strategy
Alert commercial teams to significant competitor moves (major price changes, promotions, assortment shifts)
Data Support for Data Science & Strategy
Partner with Lead Data Scientists to provide clean, high-quality datasets for model development (pricing models, demand forecasts, assortment optimization)
Conduct exploratory data analysis (EDA) to identify data quality issues, outliers, and patterns before modeling
Validate data science model outputs against business logic and historical performance; flag anomalies
Support A/B test analysis: prepare test/control groups, track KPIs during experiments, calculate statistical significance
Provide analytical support for strategic initiatives led by Senior Strategy Manager or Principal TPM
Build financial models and scenario analyses for business cases (ROI calculations, sensitivity analysis, payback period)
Identify and document data quality issues (missing data, inconsistencies, incorrect mappings) impacting analysis or models
Work with IT, BI teams, and data engineers to resolve data issues and improve pipelines
Maintain data dictionaries and documentation for key datasets used by team
Insight Generation & Proactive Analysis
Proactively surface insights and opportunities from data exploration (don't just answer questions - identify what questions should be asked)
Conduct regular "health checks" on business performance, flagging issues before they escalate
Present findings to commercial teams (category managers, regional leads, merchandising) in weekly or monthly forums
Create clear, actionable data visualizations (charts, dashboards) that tell stories and drive decisions
Translate complex analysis into simple business language without losing nuance
Build self-serve dashboards (Tableau, Power BI) empowering commercial teams to explore data independently
WHAT WE ARE LOOKING FOR
Expert SQL: writes and optimizes complex queries (CTEs, window functions, subqueries) on large datasets
Advanced Excel: pivots, INDEX/MATCH, complex formulas; VBA automation a plus
Python or R (plus) for data manipulation and analysis beyond Excel
Strong statistical foundation: descriptive stats, regression, hypothesis testing; validates results against business logic
5–8 years' experience in commercial analytics, BI, or FP&A; within retail, e-commerce, travel, hospitality, or FMCG
Deep commercial understanding: pricing, margins, promotions, inventory, merchandising, and P&L; mechanics
Insight-driven analyst: translates complex data into actionable business recommendations
Financial analysis expertise: P&L; performance, variance analysis, and financial modeling
Executive-level communication: confident presenting to CCO/CFO and handling challenge
Clear written communication: concise executive summaries, analytical memos, dashboard commentary
Strong data visualization: creates clear, decision-ready charts without overcomplication
Audience-aware communicator: adapts message for executives, commercial teams, and technical stakeholders
Bias for speed and ownership: delivers high-quality analysis in hours/days, not weeks
Resilient under pressure: thrives with tight deadlines and last-minute senior requests
Sound business judgment & curiosity: focuses on what matters, makes pragmatic assumptions, and probes root causes
Fluency in English (written & spoken)
Preferred Qualifications
Bachelor's degree in quantitative field (Economics, Finance, Engineering, Mathematics, Statistics, Business Analytics) or equivalent practical experience
Experience with BI tools (Tableau, Power BI, Looker) - can build dashboards, not just consume them
Previous exposure to data science or ML projects - understands enough to support DS teams and validate outputs
Background in pricing analytics, revenue management, or merchandising analytics would be a plus
Python proficiency is a plus
Having a background in management consultancy or tech company would be preferred
#J-*****-Ljbffr