.OverviewAre you ready to work as a Data Scientist for our Global business in Barcelona? You will be part of a growing team to create and support global digital developments for PepsiCo around topics related with Revenue Management, Supply Chain, Manufacturing, Logistics.You will be part of a collaborative interdisciplinary team around data, where you will be responsible for building deployable statistical/machine learning models, starting from the discovery phase. You will work with process owners, product owners, and final business users. This will provide you the correct visibility and understanding of criticality of your developments. You will be an internal ambassador of the team's culture around data and analytics. You will provide stewardship to colleagues in the areas that you are a specialist or you are specializing.The project builds the necessary setup for FP&A (Financial Planning and Analysis), refines/creates the data transformation capabilities, and creates/evolves Machine Learning/Artificial Intelligence models for P&L and OCF based on both internal and external data sources. The objective is to build forecast models for P&L (NOPBT – Net Operating Profit before Tax) and OCF (Operating Cash Flow).Replace the manual forecast which removes the bias introduced by the planners in the forecast.Augment the manual planners with estimates to enable forecasts by exception.Increase granularity at which the forecasting is done without trading off the accuracy.ResponsibilitiesYour Day to Day with us:Ensure data access for discovery and proper data is prepared for model usage partnering with data engineers.Partner with ML engineers working on industrialization.Coordinate work activities with Business teams and other IT services.Guide the use of the Platform toolset and focus on 'the art of the possible' demonstrations to the business.Communicate with team members in the process of service design, training, and knowledge transfer.Support large-scale experimentation and build data-driven models.Set metrics to evaluate analytics solutions given a use case.Refine requirements into modeling problems.Influence product teams through data-based recommendations.Research in methodologies.Generate reusable packages or libraries.Create documentation for insights and knowledge transfer.QualificationsWhat experience do you need to succeed?Building solutions in the FP&A Financial Planning and Analysis space.Statistical/ML techniques to solve forecasting, supervised (regression, classification), and unsupervised problems.ETL and/or data wrangling techniques. Fluent in SQL syntax.In particular, work experience in time series forecasting using ML is a plus.Developing business problem-related statistical/ML modeling with industry tools with primary focus on Python development.In particular, work experience deploying analytics into production using Azure or Databricks environments is a plus