.OverviewAre you ready to drive PepsiCo's digital evolution and accelerate transformation across our global operations?With data deeply embedded in our DNA, PepsiCo Data & Analytics transforms data into consumer delight.
We build and organize business-ready data that allows PepsiCo's leaders to solve their problems with the highest degree of confidence.
Our platform of data products and services ensures data is activated at scale, enabling new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise.The Data Science Pillar in D+Ai will be the organization where Data Scientists and ML Engineers report to in the broader D+Ai Organization.
DS will lead, facilitate, and collaborate on the larger DS community in PepsiCo, providing the talent for the development and support of DS components and its life cycle within D+A Products.
DS will also support 'pre-engagement' activities as requested and validated by the prioritization framework of D+Ai.ResponsibilitiesYour day-to-day with us:Project Participation: Contribute to digital projects, collaborating with team members to ensure successful project execution.Subject Matter Expertise: Provide technical knowledge and support for one or more digital projects.Innovation Contribution: Actively participate in innovation activities, exploring and implementing cutting-edge data science techniques.Collaboration with Product Managers and Data Engineers.Solution Support: Support ML engineers in transitioning developed models into industrialized, scalable solutions ready for production.Cross-Functional Coordination: Coordinate work activities with business teams, IT services, and other relevant stakeholders to ensure cohesive project progress and integration.Platform Toolset Utilization: Support the adoption and use of the Platform toolset, showcasing 'the art of the possible' through demonstrations to business stakeholders as needed.Support Experimentation: Assist in large-scale experimentation, building and validating data-driven models to solve complex business problems.Set KPIs and Metrics: Help define key performance indicators (KPIs) and metrics to evaluate the effectiveness of analytics solutions for specific use cases.Refine Requirements: Translate business requirements into well-defined modeling problems, ensuring clarity and feasibility for the data science team.Influence Through Data: Provide data-based recommendations to influence product teams and drive strategic decision-making.Research and Development: Stay current with state-of-the-art methodologies, conducting research to integrate the latest advancements into the team's work.Documentation and Knowledge Transfer: Create comprehensive documentation for learnings and ensure effective knowledge transfer within the team and across the organization