.OverviewAre you ready to take on a unique industry role where you will have a chance to lead a team building and growing the proprietary forecasting engine for a global giant like PepsiCo?Join us as a Senior Data Science Manager, where your expertise in creating deployable statistical and machine learning models will drive global digital advancements for PepsiCo. One of your main focuses will be demand forecasting, shaping developments in critical areas such as revenue management, supply chain, manufacturing, and logistics.You will be part of a collaborative interdisciplinary team around data, where you will be responsible for leading a team that is in charge of building and sustaining the PepsiCo forecasting engine, consisting of deployable statistical and machine learning models, mostly for time series forecasting in the context of integrated business planning and beyond. You will work closely with process owners, product owners, and final business users. This will provide you with the correct visibility and understanding of the criticality of your developments.ResponsibilitiesYour day-to-day with us:Develop a sustainable analytical toolkit (primarily, PepsiCo forecasting engine) in the form of libraries or components that can be deployed via configuration for specific projects and datasets. It would include mostly time series forecasting and recommendation engines. Functionalities should include hypothesis testing to get the best configuration (mostly in back-testing and time travel for time series forecasting), and prediction.Manage requests coming from various market-specific teams through data-driven prioritization by keeping the long-term product vision in mind.Be able to work in Azure and Databricks environments, but be ready to switch to some other.Partner with data engineers and other data teams to ensure data readiness and accessibility for model consumption.Coordinate work activities with Business teams, other IT services, and other teams, if required.Drive the use of the Platform toolset and also focus on 'the art of the possible' demonstrations to the business as needed.Communicate with business stakeholders in the process of service design, training, and knowledge transfer.Support large-scale hypothesis testing and build data-driven models.Set KPIs and metrics to evaluate analytics solution given a particular use case.Translate requirements into modelling problems.Influence product teams through data-based recommendations.Research and bring to practice state-of-the-art methodologies.Create documentation for learnings and knowledge transfer.Create reusable packages or libraries.Manage team members' objectives and expectations.Organize day-to-day tasks and mid-term roadmap of releases, using ADO board or any other tool