.OverviewThe Integrated Business Planning (IBP) process aims to transform business decision making that will result in profitable and sustainable growth balancing strategic, financial and operational objectives. It is a cross-functional, rolling planning process integrating product, marketing, sales, supply chain, partner, and financial plans into one aligned plan for the business through a global standard. This single set of numbers will leverage next-generation orchestration and analytic technology coupled with data and digital savvy PepsiCo talent, and – in most markets – an enabling decision-support tool, ensuring agile and fast decision-making to take us to the next level above the competition.You will be part of a growing team to create and support global digital developments for PepsiCo around topics related to Finance Management, Revenue Management, Supply Chain, Manufacturing, and 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. As a member of the Data Science team, you will explore and analyze data coming from a wide variety of sources (both internal and external) to attain a deep understanding of it, both from data science and from a business perspective. This will enable you to create accurate forecasting models as well as to explain the insights of the results obtained to the business stakeholders.ResponsibilitiesExplore and analyze data to gain powerful insights, both from a data science and a business perspectiveCreate, evaluate, compare, and improve forecasting models to attain a business-grade AI forecastIntegrate your forecasting models into the existing DataOps/MLOps environment so that they are scalable, robust, and production-readyExplain your findings and conclusions both to a Data Science audience as well as to a business audience. Always keep in mind the business relevance of your workIntegrate into a multidisciplinary team, collaborate with other members to improve the overall team efficiency and resultsSupervise the models in production to detect failures or performance issues and debug them if neededCreate and maintain documentation for insights and knowledge transferResearch and apply novel technologies that may improve the existing processesQualificationsBuilding complete solutions in machine learning, including data exploration, feature engineering, model design and evaluation, and deployment in productionStatistical/ML techniques and concepts
