Senior Data Scientist

Overview

Your role will be based in Plano, to contribute to DS and Advanced Analytics to create AI/ML solutions and to assess the AI safety risks and opportunities for reusability of DS projects aligned with PEP roadmap within the global digital transformation journey within PepsiCo. A wide range of functions including revenue management, supply chain, manufacturing, logistics will be impacted by the adoption of AI technologies, thus augmenting and assisting our people.

You will be part of a collaborative interdisciplinary team around data and analytics, where you where you will be responsible for shape, assess and design new products in the domain of ML/AI to support the development of digital solutions to create business decision making, 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 to business stakeholders and S&T functional leaders. On the other hand, you will work closely with other data scientists to transfer your findings to ensure the scalability, robustness and reusability of the solutions that you develop.

You will be an internal ambassador of the team’s culture around data and analytics. You will provide stewardship to colleagues in the areas of AI that you master and further lead the onboarding of new developments in AI within the PepsiCo domain.

Responsibilities

• Active contributor to code development in projects and services.
• Active contributor in innovation activities.
• Create documentation for learnings and knowledge transfer.
• Create reusable packages or libraries.
• 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.
• Occasionally, coordinate work activities with Business teams, other services and as required.
• Communicate with data scientists and other S&T stakeholders in the process of service design, training and knowledge transfer.
• Support large-scale experimentation and build analytical 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.

Compensation and Benefits:

• The expected compensation range for this position is between $89,000 - $149,000.
• 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 10% of annual salary paid out annually.
• 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

Minimum

• Master's Degree in Computer Science, Data Science, Mathematics, Statistics, Computational Linguistics, or a related quantitative field.
• Preferred: PhD in Computer Science, Machine Learning, Statistics, Computational Linguistics, or related fields, with specialization in Generative AI, NLP, or NLU.
• Minimum: 4+ years of hands-on experience building, deploying, and evaluating generative AI models in a research or industry setting, with a strong focus on NLP/NLU applications.
• Ideal: 4+ years of experience, with demonstrable success in developing Generative AI solutions focused on NLP/NLU, and delivering tangible business value.
• Advantageous Familiarity with CPG (Consumer Packaged Goods) industry dynamics and challenges.
• NLP and NLU: Solid experience in Natural Language Processing and Understanding techniques, including tokenization, stemming, named entity recognition, part-of-speech tagging, sentiment analysis, intent classification, and more.
• Generative AI models (with NLP/NLU):
• Experience with architectures like GANs, VAEs, Diffusion models, or advanced Transformer-based LLMs applied to language tasks.
• Use cases: text generation, text summarization, machine translation, question answering, dialogue systems, etc.
• Deep Learning Frameworks: Proficiency in Tensorflow, PyTorch, or similar frameworks.
• Python: Es