Machine Learning Engineer
hace 1 mes
About PepsiCo
PepsiCo is a global food and beverage leader with a diverse portfolio of 22 brands that generate over $1 billion in annual retail sales. Our main businesses, including Quaker, Tropicana, Gatorade, Frito-Lay, and Pepsi-Cola, create hundreds of enjoyable foods and beverages loved worldwide. At PepsiCo, you'll have the best of both worlds: an entrepreneur's mindset and the reach and resources of a global company. Our collaborative culture and worldwide presence generate a stream of new opportunities to define the future and propel your life's work.
About Data & Analytics
PepsiCo Data & Analytics transforms data into consumer delight by building and organizing business-ready data that allows our leaders to solve problems with 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.
Job Description
Your role will be part of a growing team based in Barcelona, Vitoria, Mexico City, or Hyderabad, creating and supporting global digital developments for PepsiCo. These developments will focus on topics like revenue management, supply chain, manufacturing, and logistics. You'll work closely with process owners, product owners, and business users to provide visibility and understanding of critical developments. You'll be an internal ambassador of the team's culture around data and analytics, providing stewardship to colleagues in areas where you're a specialist or specializing.
Responsibilities
Partner with data engineers to ensure data access for discovery and proper data preparation for model consumption.
Partner with ML engineers working on industrialization.
Coordinate work activities with Business teams, other IT services, and as required.
Drive the use of the Platform toolset and 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 experimentation and build data-driven models.
Set KPIs and metrics to evaluate analytics solutions given a particular use case.
Refine requirements into modeling problems.
Influence product teams through data-based recommendations.
Research in state-of-the-art methodologies.
Create documentation for learnings and knowledge transfer.
Create reusable packages or libraries
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