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PepsiCo is a global food and beverage leader with a product portfolio including 22 brands that generate more than $1 billion each in annual retail sales. Our main businesses – Quaker, Tropicana, Gatorade, Frito-Lay and Pepsi-Cola – make hundreds of enjoyable foods and beverages that are loved throughout the world.
At PepsiCo, you get the best of both worlds: an entrepreneur’s mindset plus reach and resources. Our collaborative culture and worldwide presence generate a stream of new opportunities to define the future and propel your life’s work. Bring your unique perspective. Bring curiosity. Bring ingenuity, and drive. We’ll give you a platform to be daring on a global scale.
About Data & Analytics
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. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise.
Your role will be to be part of growing team called “Digital Hub”, based in Barcelona, to create and support global digital developments for PepsiCo. These developments will be around topics like revenue management, supply chain, manufacturing, logistics.
You will be part of a collaborative interdisciplinary team around data, where you will be responsible of building deployable statistical/machine learning models, starting from the discovery phase . You will work closely with process owners, product owners and final business users. This will provide you the correct visibility and understanding of criticality of your developments.
You will be an internal ambassador of the team’s culture around data and analytics. You will provide stewardship to colleagues in the areas that you are an specialist or you are specializing.
This Role Will Support The Following Activities
- Partner with data engineers to ensure data access for discovery and proper data is prepared 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 to 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 experimentation and build data-driven models.
- Set KPIs and metrics to evaluate analytics solution given a particular use case.
- Refine requirements into modelling 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.
- 4+ years’ experience building solutions in the revenue management or in the supply chain space.
- 4+ years working in a team to deliver production level analytic solutions. Fluent in git (version control). Understanding of Jenkins, Docker are a plus.
- 4+ years’ experience in ETL and/or data wrangling techniques. Fluent in SQL syntaxis.
- 4+ years’ experience in Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems. Experiences with Deep Learning are a plus.
- 3+ years’ experience in developing business problem related statistical/ML modeling with industry tools with primary focus on Python or Scala development.
- Business storytelling and communicating data insights in business consumable format. Fluent in one Visualization tool.
- Strong communications and organizational skills with the ability to deal with ambiguity while juggling multiple priorities
- Experience with Agile methodology for team work and analytics ‘product’ creation. Fluent in Jira, Confluence.
- Experience with Azure cloud services is a plus.
- Experience in Reinforcement Learning is a plus.
- Experience in Simulation and Optimization problems in any space is a plus.
- Experience with Bayesian methods is a plus.
- Experience with Causal inference is a plus.
- Experience with NLP is a plus.
- Experience with working with FAIR data is a plus.
- Experience with Responsible AI is a plus.
- Experience with distributed machine learning is a plus
Not Eligible for RelocationJob Type: