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About This Role
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.
Salary Context
This $127K-$221K range is above the median for MLOps Engineer roles in our dataset (median: $172K across 22 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 11,520 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At PepsiCo, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 171 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($174K) sits 15% below the category median. Disclosed range: $127K to $221K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
PepsiCo AI Hiring
PepsiCo has 5 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Positions span Plano, TX, US, Purchase, NY. Compensation range: $221K - $221K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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