PURPOSE OF THE ROLE
The General 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
Technical Leadership & Collaboration
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 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
Pricing & Revenue Optimization Expertise
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
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📌 Global Data Scientist (Madrid)
🏢 Avolta
📍 Madrid