Data Science Director

Overview

The main objective of the Data Science SC&Ops Lead role is to implement and support globally PepsiCo’s unified D&Ai products vision and take the ownership of the analytics components in Supply Chain products. This highly strategic role requires extensive experience in managing large technical programs, and deep expertise in business applications of DS.

 

This individual will coordinate closely with PepsiCo’s D&Ai and DS LT to refine the strategic vision regarding intelligent systems applied to Supply Chain use cases. The role is accountable for executing high-impact DS Supply Chain initiatives where this professional will play a key role in creating and driving a culture that values and understands the importance of data.

Responsibilities

Scope of responsibilities for the role extends across different Supply Chain global programs with close connection to the Supply Chain transformation leads to help them build and refine the analytical roadmap for their programs. Key accountabilities include:

 

Accountable for driving effective value generating Supply Chain practices in D&Ai Data Science:

• Partner with the global D&Ai team to help define the strategic global Supply Chain vision with a focus on applying the right tools in AI, machine learning, and analytics in precise, scalable, secure, and ethical ways.
• Interact with executives across the enterprise to manage the narrative around Data Science and analytics applied to Supply Chain use cases.
• Manage a team capable of creating Data Science components and solving challenging Data Science problems across the enterprise.

Accountable for planning and execution of Data Science activities in Supply Chain programs and initiatives:

• Align with other DS leads on reusable components across initiatives.
• Conceive, plan, and prioritize Data Science and analytics programs and initiatives, consistent with the global D&Ai vision.
• Be aware of advancements in Data Science space and best practices in Supply Chain use cases world.
• Oversee and report on Data Science work under Supply Chain initiatives.

Consult on and guide the execution of Data Science and analytics problems in Supply Chain use cases green fields:

• Guide the development of experiments and pilot programs to test new Data Science capabilities in the Supply Chain domain.
• Work with the MTM DS leads and innovation leads across the PepsiCo ecosystem.

Compensation and Benefits:

• The expected compensation range for this position is between $125,900 - $249,900.
• Location, confirmed job-related skills, experience, and education will be considered in setting actual starting salary. Your recruiter can share more about the specific salary range during the hiring process.
• Bonus based on performance and eligibility target payout is 25% of annual salary paid out annually.
• and an additional target payout of 10% of annual salary is paid out over 3 years following the end of the performance period.
• Long term incentive equity may be awarded based on eligibility and performance.
• Paid time off subject to eligibility, including paid parental leave, vacation, sick, and bereavement.
• In addition to salary, PepsiCo offers a comprehensive benefits package to support our employees and their families, subject to elections and eligibility: Medical, Dental, Vision, Disability, Health, and Dependent Care Reimbursement Accounts, Employee Assistance Program (EAP), Insurance (Accident, Group Legal, Life), Defined Contribution Retirement Plan.

Qualifications

• Sc. or PhD. in Statistics, Machine Learning, Mathematics, Operations Research, Computer Science, Economics, or any other related quantitative field.
• 7 years+ of working experience in the Supply Chain domain.
• 12 years+ of working experience in a Data Science position, preferably working as a Senior Data Scientist or Enterprise Architect.
• Proven and successful track record of building and leading high-performing Data Science teams.
• Proven experience as a senior Data Scientist with a growth mindset and leading a sector-wide function.
• Deep knowledge of machine learning models and techniques and ability to use them to solve important business challenges.
• Good knowledge of data management, governance, and compliance.
• Good knowledge of modeling and visualization techniques.
• Solid understanding of statistical analysis, descriptive, predictive, and prescriptive modeling.
• Deep understanding of machine learning/AI/GenAI models.
• Strong organizational and leadership skills.
• Interpersonal skills, emotional intelligence and the ability to inspire and engage multi-functional teams in a complex and