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About This Role
Overview:
PepsiCo Data \& Analytics Overview:
With data deeply embedded in our DNA, PepsiCo Data \& Analytics transforms data into consumer delight. We build and organize business\-ready data that allows 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.
The Data Science Pillar in D\+A is our core organization that Data Scientist and ML Engineers report to in the broader D\+A Organization. Members of the Data Science Pillar will lead collaboration in the larger DS community in PepsiCo. These members are the talent powering the development and support of DS components within the life cycle of all D\+A Products. They also may support pre\-engagement feasibility activities as requested and validated by the prioritization framework of D\+A.
Position Overview:
Your role will be to be part of an established team in Plano, to create and support global digital developments for PepsiCo within our Consumer Activation space. As a Data Science Senior Analyst, you will play a pivotal role in leveraging advanced analytics and machine learning to deliver impactful solutions across critical business topics relating to our consumers.
You will be part of a collaborative interdisciplinary team around data, where you will be responsible for building deployable statistical/machine learning models, beginning from the discovery phase through implementation. You’ll collaborate closely with process owners, product managers, and end\-users to ensure your developments are aligned with business priorities and drive measurable outcomes.
You will be an internal ambassador of the team’s culture around data and analytics fostering a culture of innovation, analytics, and collaboration. You will provide stewardship to colleagues in the areas that you are a specialist or you are specializing contributing to the team’s growth and success in data and analytics.
Responsibilities:
- Contributing member in a digital project.
- Present your findings in a business friendly manner to our stakeholders.
- Build machine learning models with minimal oversight.
- Act as a subject matter expert in our digital project to other teams.
- Be a contributor in innovation activities.
- Partner with product managers in proving feedback on story points and tickets.
- Partner with data engineers to ensure data access for discovery and proper data is prepared for model consumption.
- Contribute with work activities that involve Business teams, other IT services and as required.
- Drive the use of the Platform toolset and to also focus on 'the art of the possible' demonstrations to the business as needed.
- Communicate with business stakeholders in the process of service design, training and knowledge transfer.
- Support large\-scale experimentation and build data\-driven 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.
- Create documentation for learnings and knowledge transfer.
- Support in creation reusable packages or libraries.
Compensation and Benefits:
- The expected compensation range for this position is between $80,200 \- $134,250\.
- 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 8% 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:
- 3\+ years’ experience in Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems.
- 3\+ years’ experience in developing business problem related statistical/ML modeling with primary focus on Python development.
- 2\+ years’ experience working with modern cloud platforms to power ML solutions.
- 2\+ years working in a product focused team to deliver production level analytic solutions.
- Business storytelling and communicating data insights in business consumable format. Fluent in one Visualization tool.
- Strong communications and organizational skills with the ability to deal with ambiguity while juggling multiple priorities
- Experience with Agile methodology for team work and product creation.
- Experience in Reinforcement Learning is a must.
- Experience with Pyspark is a must.
- Experience with SQL is a must.
- Fluent in git with an understanding of Jenkins or Docker a plus.
- Experience with NLP is a plus.
- Experience with LLM is a plus.
- Experience with working with FAIR data is a plus.
- Experience with Responsible AI is a plus.
- Experience with distributed machine learning is a plus
\>:
Our Company will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the Fair Credit Reporting Act, and all other applicable laws, including but not limited to, San Francisco Police Code Sections 4901\-4919, commonly referred to as the San Francisco Fair Chance Ordinance; and Chapter XVII, Article 9 of the Los Angeles Municipal Code, commonly referred to as the Fair Chance Initiative for Hiring Ordinance.
All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
PepsiCo is an Equal Opportunity Employer: Female / Minority / Disability / Protected Veteran / Sexual Orientation / Gender Identity / Age
If you'd like more information about your EEO rights as an applicant under the law, please download the available EEO is the Law \& EEO is the Law Supplementdocuments. View PepsiCo EEO Policy.
Please view our Pay Transparency Statement.
Salary Context
This $80K-$134K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At PepsiCo, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($107K) sits 44% below the category median. Disclosed range: $80K to $134K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
PepsiCo AI Hiring
PepsiCo has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Plano, TX, US. Compensation range: $134K - $156K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
Frequently Asked Questions
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