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About This Role
Are you ready to be at the forefront of innovation in the world of pizza? We are seeking a Senior Data Scientist to help drive high\-impact, data\-led decision making across Pizza Hut’s business. This role sits on the Customer Strategy \& Advanced Analytics team and applies rigorous analytics, statistical methods, and modeling to solve complex business problems using data from across the organization.
As a senior individual contributor, you will independently resolve a diverse range of complex problems where precedent may not exist, exercise strong judgment in choosing methods, and create or significantly improve the solutions, frameworks, and tooling the team relies on. You will partner closely with cross\-functional stakeholders to identify opportunities, frame business questions, build robust analytical approaches, and translate findings into clear, actionable recommendations. Join us at Pizza Hut, where we are on an incredible journey to become the most loved and fastest\-growing brand in the US and around the world.
- Partner with cross\-functional teams to identify business opportunities and independently translate ambiguous questions into structured analytical problems.
- Build, validate, and maintain analytical and predictive models to better understand customer behavior, business performance, and strategic opportunities – creating or significantly improving existing approaches.
- Lead deep\-dive analyses across multiple business domains with minimal guidance to uncover drivers of performance, diagnose issues, and quantify opportunities.
- Apply statistical methods and analytical rigor to evaluate business performance, test hypotheses, and support decision making where precedent may not exist.
- Design and assess experiments, measurement frameworks, and other analytical approaches to estimate impact and separate signal from noise.
- Develop scalable analytical frameworks, metrics, and data models that enable timely, repeatable, and decision\-ready insights across the team.
- Serve as a resource to less experienced analysts – sharing methods, reviewing approaches, and helping raise the analytical standard of the team.
- Communicate findings, assumptions, limitations, and recommendations clearly to both technical and non\-technical stakeholders, including senior leaders.
- Build presentations and executive\-ready materials that explain analytical conclusions and influence business action.
- Partner with stakeholders to improve reporting, measurement, and analytical tooling in support of strategic decision making.
Minimum Requirements
Education: Bachelor’s degree from an accredited institution in Statistics, Mathematics, Data Science, Computer Science, Economics, Engineering, or a related quantitative field, or equivalent relevant experience.
Required Experience \& Skills
- 5\+ years of experience in data science, advanced analytics, or a related quantitative field (3\+ years with a master’s degree; doctorate with relevant project experience).
- Demonstrated ability to independently understand and resolve a diverse range of complex problems where precedent may not exist, using sound judgment to select methods.
- Track record of creating or significantly improving analytical solutions, processes, and tools.
- Strong analytical and problem\-solving skills, with the ability to decompose complex business problems into measurable components while remaining grounded in business objectives.
- Strong communication skills, with a track record of clearly explaining analytical methods, findings, and tradeoffs to diverse stakeholders, including senior leaders.
- Strong SQL skills, including the ability to query complex datasets, transform data, and build analytical data models as needed.
- Proficiency in Python with hands\-on experience applying it to data analysis, statistical analysis, and modeling.
- Experience applying statistical techniques such as hypothesis testing, regression, forecasting, segmentation, or other quantitative methods in business settings.
- Experience building analytical approaches that are methodologically sound, reproducible, and scalable.
- Experience with data management, data wrangling, and exploratory analysis across large and complex datasets.
- Experience using BI or data visualization tools such as Tableau, Power BI, Domo, or similar platforms.
- Ability to collaborate effectively with stakeholders and work across cross\-functional teams to influence decisions through data.
Preferred
- Experience developing predictive, explanatory, or optimization\-oriented models in support of business decision making.
- Experience designing experiments or using causal inference, quasi\-experimental methods, or other advanced measurement approaches to evaluate business initiatives.
- Experience with customer analytics, retention, personalization, lifecycle, or marketing measurement.
- Experience working with customer, marketing, digital, financial, operational, or transactional data in a complex business environment.
- Experience building and maintaining dashboard\-based reporting through BI tools such as Tableau, Power BI, or Domo.
- Experience operationalizing analytical solutions in partnership with data engineering, product, or business teams.
- Experience acting as a resource or informal mentor to less experienced analysts.
- Advanced degree in Statistics, Mathematics, Economics, Data Science, Operations Research, or a related field.
*Salary Range: $117,800 to $132,800 annually \+ bonus eligibility.* This is the expected salary range for this position. Ultimately, in determining pay, we'll consider the successful candidate’s location, experience, and other job\-related factors.
Salary Context
This $117K-$132K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At YUM! Brands, Inc., 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 35% below the category median. Disclosed range: $117K to $132K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
YUM! Brands, Inc. AI Hiring
YUM! Brands, Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in Plano, TX, US. Compensation range: $132K - $132K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.
Frequently Asked Questions
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