Data Product Manager

PepsiCo•Plano, TX

4d•$110,700 - $185,250

About The Position

With data deeply embedded in our DNA, PepsiCo Data & Analytics (D&Ai) transforms data into consumer delight. We build and organize business-ready data and analytics that allow 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. Data Product Management is part of the Global Data, Analytics & Ai (D&AI) team. We are tasked with articulation of the vision and leading the implementation of PepsiCo’s next generation global Data infrastructure. The scope encompasses the full Strategy & Transformation agenda, leading all Data Product roadmap initiatives. We are looking for a Data Product Manager to lead the delivery of data and analytics for PepsiCo’s Consumer and Commercial data products. This individual delivers these critical dimensions: Building and executing seamlessly against data roadmaps for each data initiative. Capturing business requirements, data requirements and translating for technical teams Leading cross functional teams (PODs) to deliver, via agile artifacts and ceremonies. Being the voice of Data, Analytics and Ai to leadership and business stakeholders. Working with technical teams to meet their needs, and to provide guidance and leadership on delivery. Drive adoption of data solutions in business and continue to ensure that data and analytics meet the needs of the business use cases.

Requirements

Bachelor’s degree required
8 years of Data Product Management in business facing functions which includes strong expertise in agile and product lifecycle discipline.
3+ years of experience leading/building advanced analytics and big data solutions or building enterprise SaaS.
Deep understanding of fundamental machine-learning principles (for instance, training versus predicting, performance measurement, and overfit), as well as techniques such as random forests or neural networks
Familiarity with machine-learning technologies and tools, such as big data stack, Python, and visualization techniques
Knowledge of data architecture, modeling, and engineering concepts. Familiarity with Azure tech stack a huge plus.
Familiarity with principles and tools for data governance and stewardship
Persuasive communication skills and an ability to break down complex information into relevant and digestible points for both technical teams and the business.
Demonstrated ability to drive business-oriented and innovative solutions using data science, feature engineering and machine learning.
Highly initiative taking with ability to identify and pursue growth and opportunities.
Seasoned C-Suite presenter
Excellent leadership skills, with a team-player attitude to drive the end-to-end implementation of use cases under time pressure.
Strong people management skills with the ability to develop teams and cultivate talent, including teams composed of various experience levels.

Responsibilities

Define the product/data roadmap and capture comprehensive requirements.
Design the solution with technical teams and capture the nuances to ensure E2E optimization and efficiency.
Author technical data requirements and documents required throughout the delivery life cycle.
Manage delivery against key technical delivery milestones.
Ensure the technical teams are clear on the “why” from the perspective of the Business customer.
Work with technical teams (Data Science, Data Engineering, Stewardship, Governance, BI/Visualization) to write and steward user stories and requirements in Azure DevOps. Ensure these teams are spending optimal time at work in their specialties.
Manage the output and development of other Data Product Managers
Manage business customer expectations and seek to eliminate disconnect.
Collaboratively manage reporting that measures D&Ai execution against key programs.
Ensure data, analytics and Ai outputs meet requirements.
Translate the analytics solutions back to the business to ensure they are actionable and business can drive value from those insights.
Collaborate with business & transformation teams to embed analytics solutions and overcome implementation difficulties.
Effectively manage key stakeholders at all levels within the organization.
Foster collaborative team culture within PODs