Data Science (MLOPS) Tech Lead

Overview PepsiCo is seeking a Principal Data Scientist to help build, scale, and sustain global digital and analytics solutions across the enterprise. This role sits within PepsiCo’s Data & Analytics organization and will focus on solving complex business problems, powering digital products, and creating measurable impact across commercial, supply chain, manufacturing, logistics, sales, finance, and other enterprise domains. As a Principal Data Scientist, you will serve as a senior hands-on contributor and technical leader, translating business needs into advanced statistical and machine learning solutions that are production-ready, reliable, reusable, monitorable, and sustainable. You will partner closely with Product, Data Engineering, ML Engineering, IT, and business stakeholders to move analytical solutions from discovery and experimentation into scalable enterprise adoption. The role may also evolve into people management responsibilities as the team expands Responsibilities Lead highly complex data science initiatives from discovery through model development, deployment, monitoring, and sustained business use. Translate business problems into modeling, experimentation, simulation, optimization, and machine learning approaches with clear pathways to scalable production adoption. Design analytical solutions that are explainable, reusable, reliable, observable, and supportable in enterprise environments. Partner with Product Managers, Product Owners, Data Engineering, ML Engineering, IT, and business stakeholders to define requirements, shape roadmaps, and deliver business-ready analytics products. Work with engineering teams to industrialize models into production-ready components, services, workflows, scheduled scoring processes, and reusable patterns. Define success metrics, KPIs, model health measures, data quality checks, and business outcome metrics to evaluate performance and impact after deployment. Research and apply appropriate emerging methods in machine learning, AI, statistics, optimization, and advanced analytics. Create reusable packages, libraries, documentation, best practices, and knowledge-transfer materials that advance the broader Data Science community. Provide technical leadership, coaching, and guidance to Data Scientists, ML Engineers, and cross-functional partners. Help shape a collaborative culture focused on innovation, productization, reliability, reproducibility, and measurable business impact. Compensation and Benefits: The expected compensation range for this position is between $127,500 - $221,800. 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 12% 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 6+ years of experience building and delivering analytics, data science, or machine learning solutions in commercial, supply chain, manufacturing, logistics, finance, sales, or related business domains. 6+ years of experience applying statistical and machine learning techniques, including supervised and unsupervised learning, to solve business problems. 6+ years of hands-on experience using Python or similar programming languages for statistical or machine learning model development. 6+ years of experience with SQL, ETL, data wrangling, and preparing data for analytical or machine learning use cases. Experience designing, deploying, monitoring, and sustaining production-level or business-critical machine learning, statistical, analytics, or reporting solutions. Experience operationalizing analytical models with engineering teams, including scalability, reliability, refresh cadence, monitoring, support, and model consumption requirements. Strong understanding of model lifecycle practices, including experimentation, reproducible development, model validation, testing, documentation, version control, and performance tracking. 4+ years of experience developing and delivering solutions using cloud-based data or compute platforms for scalable model training, scoring, monitoring, or consumption. Experience with enterprise-grade analytics platforms such as Databricks, Azure-based data platforms, or similar scalable cloud environments. Experience with Agile delivery methodologies, analytics product development, and modern engineering practices such as Git. Strong communication, stakeholder management, and organizational skills, with the ability to manage ambiguity, balance multiple priorities, and translate complex technical concepts into business-consumable insights. Preferred Qualifications Experience in CPG, retail, supply chain, manufacturing, logistics, or commercial analytics environments. Experience with visualization tools, business storytelling, and executive-ready communication of analytical insights. Experience with advanced methods such as deep learning, reinforcement learning, simulation, optimization, Bayesian methods, causal inference, NLP, Responsible AI, or distributed machine learning. Experience with CI/CD, Jenkins, Docker, Kubernetes, automated testing, pipeline orchestration, model registries, feature reuse, deployment workflows, or model monitoring frameworks. Experience mentoring data scientists, leading technical teams, or preparing for people leadership responsibilities. Key Competencies Technical leadership with the ability to influence Data Science, Product, Data Engineering, ML Engineering, IT, and business teams. Product and engineering mindset, with attention to reliability, observability, reuse, maintainability, and business adoption. Strong judgment in balancing modeling sophistication with practical deployment and support needs. Disciplined approach to documentation, reproducibility, testing, knowledge transfer, and measurable business impact. Creative, collaborative, and adaptable working style in ambiguous, matrixed, and fast-moving environments.