Machine Learning Engineer
hace 2 semanas
PepsiCo is a leading global food and beverage company with a diverse portfolio of brands that generate over $1 billion in annual retail sales. Our main businesses include Quaker, Tropicana, Gatorade, Frito-Lay, and Pepsi-Cola, which make hundreds of enjoyable foods and beverages loved worldwide.
We offer a unique blend of entrepreneurial spirit and global reach, providing a platform for our employees to drive innovation and growth. Our collaborative culture and worldwide presence create a stream of new opportunities to define the future and propel your life's work.
About Data & AnalyticsData is deeply embedded in our DNA at PepsiCo. Our Data & Analytics team transforms data into consumer delight by building and organizing business-ready data that enables our leaders to solve problems with confidence.
We have a platform of data products and services that activates data at scale, driving new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise.
Job DescriptionYou 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 revenue management, supply chain, manufacturing, and logistics.
You will work closely with process owners, product owners, and business users to build deployable statistical/machine learning models, starting from the discovery phase. You will be responsible for providing stewardship to colleagues in areas where you are 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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