Lead Data Scientist

PepsiCo; Cataluña; Presencial; EUR 70.000 - 90.000; Jornada completa

Descripción de la vacante

PepsiCo Barcelona-based Data Science role focuses on leveraging advanced analytics and machine learning to deliver solutions across Consumer Insights, Revenue Management, and Supply Chain. You will build deployable models from discovery to implementation, work with product managers, data engineers, and end-users to align with business priorities, and foster a data-driven leadership culture.

You will drive experimentation, set KPIs, and create reusable packages while empowering the analytics team

Formación

• 5+ years delivering production-level analytic solutions.
• Fluent in git (version control). Understanding of Jenkins, Docker are a plus.
• 5+ years’ ETL/data wrangling experience.
• Fluent in SQL syntax.
• 3+ years’ experience in statistical/ML techniques (supervised/unsupervised).
• 3+ years’ experience in Python/Scala for modeling.
• Business storytelling and communicating data insights.
• Experience with Agile methodology and tools like ADO, Confluence.
• Experience with Reinforcement Learning is a must.
• Experience in Simulation and Optimization problems.
• Experience with PYSPARK is a must.
• Experience with NLP is a plus.
• Experience with LLM is a plus.
• Experience with FAIR data is a plus.
• Experience with Responsible AI is a plus.
• Experience with distributed machine learning is a plus.

Responsabilidades

• Act as tech lead in digital projects and contribute as a subject matter expert.
• Lead innovation activities and partner with product managers to assess DS components in roadmaps.
• Collaborate with data engineers to ensure data access for discovery and model consumption.
• Guide ML engineers to industrialize solutions.
• Communicate with stakeholders on service design, training and knowledge transfer.
• Support large-scale experimentation and data-driven models.
• Set KPIs to evaluate analytics solutions for use cases.
• Refine requirements into modelling problems.
• Influence product teams with data-driven recommendations.
• Research state-of-the-art methodologies and create reusable libraries.

Conocimientos

Git

Jenkins

Docker

SQL

Python

Scala

Visualization

Agile

Communication

Reinforcement Learning

PySpark

NLP

LLM

Responsible AI

Distributed ML

Herramientas

Jenkins

Docker

PySpark

Descripción del empleo

Overview

Your role will be to be part of growing team based in Barcelona, to create and support global digital developments for PepsiCo. As a Data Science, you will play a pivotal role in leveraging advanced analytics and machine learning to deliver impactful solutions across critical business topics such as Consumer Insights, Revenue Management, Supply Chain, Manufacturing, and Logistics.

You will be part of a collaborative interdisciplinary team around data, where you will be responsible of building deployable statistical/machine learning models, beginning from the discovery phase through to implementation. You’ll collaborate closely with process owners, product managers, and end-users to ensure your developments are aligned with business priorities and drive measurable outcomes

You will be an internal ambassador of the team’s culture around data and analytics fostering a culture of innovation, analytics, and collaboration fostering a culture of innovation, analytics, collaboration, and being a data driven leader . You will provide stewardship to colleagues in the areas that you are a specialist or you are specializing contributing to the team’s growth and success in data and analytics.

Responsibilities

• Contributing member in a digital project acting as tech lead for the project.
• Act as a subject matter expert in one other digital project.
• Be the lead contributor in innovation activities
• Partner with product managers in taking DS requirements and assessing DS components in roadmaps.
• Partner with data engineers to ensure data access for discovery and proper data is prepared for model consumption.
• Lead ML engineers to industrialize solutions.
• Contribute with work activities that involve 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.
• Support in creation reusable packages or libraries.

Qualifications

• 5+ years working in a team to deliver production level analytic solutions.
• Fluent in git (version control). Understanding of Jenkins, Docker are a plus.
• 5+ years’ experience in ETL and/or data wrangling techniques.
• Fluent in SQL syntaxis.
• 3+ 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 ADO, Confluence.
• Experience with Reinforcement Learning is a must.
• Experience in Simulation and Optimization problems in any space is a must.
• Experience with PYSPARK is amust.
• Experience with NLP is a plus.
• Experience with LLM 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