Machine Learning Engineer

hace 4 semanas


Burgos, Burgos, España Pepsico A tiempo completo

About PepsiCo:

PepsiCo is a global leader in the food and beverage industry, with a diverse portfolio of 22 brands that generate over $1 billion in annual retail sales.

Our main businesses—Quaker, Tropicana, Gatorade, Frito-Lay, and Pepsi-Cola—offer a wide range of enjoyable foods and beverages that are loved by consumers around the world.

At PepsiCo, you'll have the opportunity to work in a collaborative environment with a global presence, generating a stream of new opportunities to define the future and propel your career.

Bring your unique perspective, curiosity, and ingenuity to the table, and we'll provide you with a platform to be daring on a global scale.

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.

Your Role:

You'll 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 the criticality of your 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 are 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 and other IT services 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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