Senior Data Scientist – Databricks, Pricing & Revenue Growth Management

• Design, develop, and optimize advanced Pricing and Revenue Growth Management (RGM) models to support strategic business decisions
• Build and validate price-demand elasticity models, including own-price and cross-price elasticities
• Develop predictive models using Machine Learning, statistical methods, and econometric techniques to analyze demand, pricing, and consumer behavior
• Perform time series analysis, demand forecasting, and mathematical optimization to identify business opportunities
• Conduct feature engineering using pricing, sales volume, seasonality, product, channel, and market data
• Design and execute experiments, backtesting, sensitivity analyses, and statistical validations to ensure model reliability
• Evaluate model robustness, temporal stability, potential biases, and underlying statistical assumptions
• Develop pricing scenarios and simulations to assess the impact of commercial strategies
• Collaborate with business stakeholders, Data Engineers, and Machine Learning Engineers to translate business challenges into data-driven solutions
• Communicate complex analytical findings and actionable recommendations to technical and non-technical stakeholders

• 4+ years of experience as a Data Scientist, ideally working on Pricing, Revenue Growth Management (RGM), demand modeling, forecasting, or optimization
• Advanced proficiency in Python and SQL
• Strong knowledge of applied statistics, econometrics, and supervised Machine Learning
• Hands-on experience with price elasticity modeling, demand modeling, price-response curves, or equivalent approaches
• Solid understanding of own-price and cross-price elasticity
• Experience with regression models, regularization techniques, and statistical inference
• Strong background in time series analysis and forecasting
• Experience with mathematical optimization, scenario modeling, and sensitivity analysis
• Proficiency in feature engineering using pricing, volume, seasonality, and commercial variables
• Experience designing experiments, performing backtesting, and conducting statistical validation
• Hands-on experience with Databricks, Spark/PySpark, and MLflow
• Experience developing and validating statistical and Machine Learning models that support business decision-making
• Experience working with sales datasets involving pricing, volume, products, channels, geographies, and time dimensions
• Ability to build models across multiple levels of granularity and assess their statistical robustness and stability over time
• Strong analytical and problem-solving skills, with the ability to translate business challenges into mathematical and predictive models
• Excellent communication and data storytelling skills, with experience presenting insights to technical and business stakeholders
• Experience collaborating with cross-functional teams, including business stakeholders, Data Engineers, and Machine Learning Engineers
• Nice-to-have: Experience working with Large Language Models (LLMs)
• Nice-to-have: Knowledge of AI agents and agentic systems
• Nice-to-have: Familiarity with MLOps practices and tools
• Nice-to-have: Experience deploying and maintaining Data Science and AI solutions in production environments
• Nice-to-have: Knowledge of job orchestration, computing infrastructure, deployments, monitoring, and maintenance
• Nice-to-have: Experience working with event data, user tracking, and application tagging
• Nice-to-have: Ability to collaborate effectively with Product, Engineering, and Business teams

• Remote-first culture – work from anywhere
• In-Company English Lessons
• Wellhub or sports club stipend
• AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
• Food credits via Pedidos Ya
• Birthday off + an extra vacation week (Mutt Week)
• Referral bonuses
• Annual Mutters' Trip
• Monthly Childcare Reimbursement