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About This Role
Now Brewing – Lead Data Scientist, Data \& Analytics! \#tobeapartner
From the beginning, Starbucks set out to be a different kind of company. One that not only celebrated coffee and the rich tradition, but that also brought a feeling of connection. We are known for developing extraordinary leaders who share this passion and are guided by their service to others.
Are you driven to uncover meaningful insights and build data\-powered solutions that shape the future of the world’s leading coffeehouse brand?
As a Lead Data Scientist on the Digital Data Science team, you will lead the development of advanced AI/ML models powering personalization \& recommendations for Starbucks digital surfaces such as the mobile app, web, digital menu boards and digital drive thru screens. You will drive the evolution of existing models and apply rigorous statistical methods to help achieve 1:1 personalization at the customer level, contextual personalization based on external signals and operational personalization that aligns with our ecosystem of stores. Your work will directly impact millions of Starbucks customers and coffee occasions at scale.
The D\&A organization spans four key pillars: Customer, Retail Operations, Supply Chain, and Enterprise. Each domain is focused on solving unique and complex challenges through data science. Whether personalizing marketing campaigns, optimizing store operations, improving inventory flow, or unlocking enterprise\-wide initiatives, our data scientists apply advanced analytics, experimentation, and modeling to elevate the Starbucks experience. This centralized recruiting approach allows us to match candidates with the most relevant opportunities based on their skills and interests, ensuring alignment with business needs and personal growth. Join a team where data meets purpose to shape what’s next for Starbucks.
*As a lead data scientist, you will…*
- Lead the design and development of scalable data science solutions from the start to the finish by utilizing machine learning, and statistical analysis. Build scalable processes for cleaning, aggregating, and loading data.
- Understanding business initiatives, challenges, questions, concerns, or ideas, transforming them into successful data science solutions, and monitor data science metrics and KPIs
- Mentor data analysts \& data scientists on best practices, processes, frameworks, and KPIs
- Contribute to project planning, and assist the team with estimations, timelines, and prioritization. Assists in developing the analytical roadmap, data, and technology strategy.
- Own the end\-to\-end lifecycle of AI/ML solutions, from ideation and prototyping to production deployment, ensuring robust offline evaluation and real\-time performance monitoring.
*We’d love to hear from people with:*
- Education: BS\+ with a concentration in a quantitative discipline \- Stats, Math, Comp Sci, Engineering, Econ, Quantitative Social Science or similar discipline. Master’s and/or PhD preferred
- Minimum of 8\+ years of progressive experience in data science, with a proven track record of end\-to\-end data product and model deployment that delivers measurable business impact.
- Strong foundation in machine learning and statistical techniques, including regression, classification, clustering, and causal inference
- Demonstrated track record of delivering AI/ML models into production with measurable business impact, including expertise in evaluation techniques (A/B testing, lift analysis) and model governance
- Strong problem\-solving skills with the ability to influence leadership teams at different levels
- Strong SQL, databases, and ETL / ELT skills required including cleaning and managing data.
- Advanced competency and expertise in Python, R, or some combination
- Experience working with distributed data processing frameworks such as Spark and Databricks, and languages such as Java, PySpark, or Scala
- Strong collaboration instincts – desire \& ability to work with partners to achieve the best outcome for the business.
- Excellent communication, presentation, and storytelling skills. The role requires effective communication with colleagues, stakeholders, and leaders from diverse backgrounds.
- Proven ability to manage multiple, time\-sensitive projects and competing priorities simultaneously to drive projects to completion with minimal guidance and high attention to details.
*Preferred skills:*
- Experience building personalization and recommendation systems (e.g., collaborative filtering, content\-based, or hybrid approaches) for digital platforms or mobile apps.
- Familiarity with ranking algorithms, reinforcement learning, and contextual bandits for optimizing customer experiences.
- Hands\-on experience with model deployment in production environments, including monitoring, retraining, and performance optimization at scale.
- Strong understanding of offline and online evaluation techniques for recommendation systems (e.g., precision/recall, NDCG, A/B testing).
- Knowledge of feature engineering for behavioral and transactional data, and experience leveraging real\-time data streams for model updates.
As a Starbucks partner, you (and your family) will have access to medical, dental, vision, basic and supplemental life insurance, and other voluntary insurance benefits. Partners have access to short\-term and long\-term disability, paid parental leave, family expansion reimbursement, paid vacation from date of hire\*, sick time (accrued at 1 hour for every 25 hours worked), eight paid holidays, and two personal days per year. Starbucks also offers eligible partners participation in a 401(k) retirement plan with employer match, a discounted company stock program (S.I.P.), Starbucks equity program (Bean Stock), incentivized emergency savings, and financial well\-being tools. Additionally, Starbucks offers 100% upfront tuition coverage for a first\-time bachelor’s degree through Arizona State University’s online program via the Starbucks College Achievement Plan, student loan management resources, and access to other educational opportunities. You will also have access to backup care and DACA reimbursement. Starbucks will comply with any applicable state and local laws regarding employee leave benefits, including, but not limited to providing time off pursuant to the Colorado Healthy Families and Workplaces Act, and in accordance with its plans and policies. This list is subject to change depending on collective bargaining in locations where partners have a certified bargaining representative. For additional information regarding partner perks and more detailed information about benefits, go to starbucksbenefits.com.
- If you are working in CA, CO, IL, LA, ME, MA, NE, ND or RI, you will accrue vacation up to a maximum of 120 hours (190 in CA) for roles below director and 200 hours (316 in CA) for roles at director or above. For roles in other states, you will be granted vacation time starting at 120 hours annually for roles below director and 200 hours annually for roles director and above.
The actual base pay offered to the successful candidate will be based on multiple factors, including but not limited to job\-related knowledge/skills, experience, geographical location, and internal equity. At Starbucks, it is not typical for an individual to be hired at the high end of the range for their role, and compensation decisions are dependent upon the facts and circumstances of each position and candidate.
We believe we do our best work when we're together, which is why we're onsite four days a week.
Join us and inspire with every cup. Apply today!
*All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, physical or mental disability, sexual orientation, marital status, military or veteran status, gender identity and expression, genetic information, or any other factor protected by law.*
*We are committed to creating a diverse and welcoming workplace that includes partners with diverse backgrounds and experiences. We believe that enables us to better meet our mission and values while serving customers throughout our global communities. People of color, women, LGBTQIA\+, veterans and persons with disabilities are encouraged to apply.*
*Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal state and local ordinances. Starbucks Corporation is committed to offering reasonable accommodations to job applicants with disabilities. If you need assistance or an accommodation due to a disability, please contact us at* *applicantaccommodation@starbucks.com**.*
Salary Context
This $146K-$243K range is above the 75th percentile for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Starbucks, 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 $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($195K) sits 5% below the category median. Disclosed range: $146K to $243K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Starbucks AI Hiring
Starbucks has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Seattle, WA, US, New York, NY, US, Nashville, TN, US. Compensation range: $243K - $244K.
Location Context
AI roles in Seattle pay a median of $223,600 across 678 tracked positions. That's 22% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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