Senior Data Science Engineer Jobs
Senior Data Science Engineer jobs are open across technology, finance, healthcare, and e-commerce, from mid-level to staff and principal, with specializations in machine learning infrastructure, large-scale data pipelines, and applied modeling. Find a role that fits from the openings below and apply directly.
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Now Brewing – director of data science – Coffeehouse 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.
Join a transformative journey at the intersection of data, technology, and operations.
This strategic leadership role is pivotal in shaping how data science drives store development, staffing, deployment, experimentation, and market decisions across our coffeehouse network. You will lead multiple high-impact data science teams focused on building decision-support systems, optimization models, and experimentation frameworks that improve partner experience, operational efficiency, and business outcomes at scale.
As a director of data science, you will bring a strong product and people leadership mindset, balancing deep technical understanding with the ability to translate complex analytics into practical, actionable solutions. You will be a visible advocate for data science as a core capability for operational excellence and continuous improvement. This is a unique opportunity to lead a talented organization, influence enterprise decisions, and materially impact how Starbucks plans, operates, and learns.
As a director of data science – Coffeehouse Analytics, you will…
- Lead End-to-End Decision Science for Operations: Own data science strategy across staffing, scheduling, routing and deployment, market planning analytics, and coffeehouse testing—ensuring models and insights directly inform real-world decisions.
- Build and Scale High-Impact Teams: Lead, mentor, and grow multiple teams of data scientists and managers, fostering strong technical rigor, product thinking, and business partnership.
- Operational Planning & Optimization Leadership: Guide the development of predictive, optimization, and diagnostic models that support labor planning, capacity management, deployment tradeoffs, and network performance.
- Experimentation & Measurement Excellence: Oversee experimentation design, causal inference, and an internal test-and-learn framework that enables rapid, trustworthy learning across coffeehouses and markets.
- Translate Analytics into Action: Partner closely with operations, finance, engineering, and field leaders to turn complex analytics into clear recommendations, tools, and decision frameworks.
- Influence Through Storytelling: Communicate insights, tradeoffs, and model outputs in compelling ways to senior leaders, helping shape strategy, prioritization, and investment decisions.
- Champion Adoption and Trust: Drive adoption of analytics products by ensuring they are usable, explainable, and embedded in operational workflows.
- Build Durable Data Science Products: Apply systems thinking to design scalable, reusable analytical products that move beyond one-off analyses and support long-term capability building.
- Strengthen Cross-Functional Collaboration: Work closely with data engineering, tools, and platform teams to ensure analytics solutions are production-ready, reliable, and extensible.
We’d love to hear from people with:
- Proven experience leading data science or advanced analytics teams, ideally supporting retail operations, store development, or labor.
- Strong background in applied analytics, including forecasting, optimization, experimentation, and decision-support systems.
- Experience building and scaling analytical products that are used in day-to-day business decision-making.
- In-depth experience with Python and reviewing production-quality analytical code.
- Ability to translate complex quantitative work into clear, actionable insights for executive and operational audiences.
- Strong cross-functional partnership and influencing skills, with a track record of driving outcomes through collaboration.
- Experience mentoring and developing data scientists and people leaders.
- Comfort operating in ambiguity and helping teams move from problem framing to scalable solutions.
- Experience driving the evolution from descriptive reporting to predictive and prescriptive decision support, including demand forecasting, capacity diagnostics, and optimization under real‑world constraints.
- Comfort balancing statistical rigor, AI innovation, and practical business constraints.
Join us and help shape the future of data-driven operations at Starbucks. Apply today!
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!
Starbucks Coffee Company is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, or protected veteran status, or any other characteristic protected by law.
Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal, state and local ordinances.
Starbucks Coffee Company 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 or 1(888) 611-2258.
Now Brewing – director of data science – Coffeehouse 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.
Join a transformative journey at the intersection of data, technology, and operations.
This strategic leadership role is pivotal in shaping how data science drives store development, staffing, deployment, experimentation, and market decisions across our coffeehouse network. You will lead multiple high-impact data science teams focused on building decision-support systems, optimization models, and experimentation frameworks that improve partner experience, operational efficiency, and business outcomes at scale.
As a director of data science, you will bring a strong product and people leadership mindset, balancing deep technical understanding with the ability to translate complex analytics into practical, actionable solutions. You will be a visible advocate for data science as a core capability for operational excellence and continuous improvement. This is a unique opportunity to lead a talented organization, influence enterprise decisions, and materially impact how Starbucks plans, operates, and learns.
As a director of data science – Coffeehouse Analytics, you will…
- Lead End-to-End Decision Science for Operations: Own data science strategy across staffing, scheduling, routing and deployment, market planning analytics, and coffeehouse testing—ensuring models and insights directly inform real-world decisions.
- Build and Scale High-Impact Teams: Lead, mentor, and grow multiple teams of data scientists and managers, fostering strong technical rigor, product thinking, and business partnership.
- Operational Planning & Optimization Leadership: Guide the development of predictive, optimization, and diagnostic models that support labor planning, capacity management, deployment tradeoffs, and network performance.
- Experimentation & Measurement Excellence: Oversee experimentation design, causal inference, and an internal test-and-learn framework that enables rapid, trustworthy learning across coffeehouses and markets.
- Translate Analytics into Action: Partner closely with operations, finance, engineering, and field leaders to turn complex analytics into clear recommendations, tools, and decision frameworks.
- Influence Through Storytelling: Communicate insights, tradeoffs, and model outputs in compelling ways to senior leaders, helping shape strategy, prioritization, and investment decisions.
- Champion Adoption and Trust: Drive adoption of analytics products by ensuring they are usable, explainable, and embedded in operational workflows.
- Build Durable Data Science Products: Apply systems thinking to design scalable, reusable analytical products that move beyond one-off analyses and support long-term capability building.
- Strengthen Cross-Functional Collaboration: Work closely with data engineering, tools, and platform teams to ensure analytics solutions are production-ready, reliable, and extensible.
We’d love to hear from people with:
- Proven experience leading data science or advanced analytics teams, ideally supporting retail operations, store development, or labor.
- Strong background in applied analytics, including forecasting, optimization, experimentation, and decision-support systems.
- Experience building and scaling analytical products that are used in day-to-day business decision-making.
- In-depth experience with Python and reviewing production-quality analytical code.
- Ability to translate complex quantitative work into clear, actionable insights for executive and operational audiences.
- Strong cross-functional partnership and influencing skills, with a track record of driving outcomes through collaboration.
- Experience mentoring and developing data scientists and people leaders.
- Comfort operating in ambiguity and helping teams move from problem framing to scalable solutions.
- Experience driving the evolution from descriptive reporting to predictive and prescriptive decision support, including demand forecasting, capacity diagnostics, and optimization under real‑world constraints.
- Comfort balancing statistical rigor, AI innovation, and practical business constraints.
Join us and help shape the future of data-driven operations at Starbucks. Apply today!
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!
Starbucks Coffee Company is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, or protected veteran status, or any other characteristic protected by law.
Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal, state and local ordinances.
Starbucks Coffee Company 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 or 1(888) 611-2258.
Senior Data Science Engineer Jobs by Experience Level
Top Cities Hiring Senior Data Science Engineers
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Find JobsSenior Data Science Engineer Job Market
Who's Hiring
- Amazon30
- Capital One23
- Meta15
- Deloitte12
- Information Technology Senior Management Forum12I
Top Industries Hiring
- Technology & Software68
- Retail27
- Education24
- Banking & Financial Services22
- Consulting & Professional Services19
What Employers Look For
The qualifications that appear most often in senior data science engineer jobs.
- 5 or more years of experience in data science, machine learning, or a related engineering discipline
- Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Hands-on experience designing and deploying production-grade machine learning pipelines
- Strong command of distributed computing tools including Spark, Databricks, or equivalent platforms
- Experience with cloud platforms such as AWS, Google Cloud, or Azure for data and model infrastructure
- Bachelor's or master's degree in computer science, statistics, mathematics, or a closely related field
Tips for Your Senior Data Science Engineer Job Search
Quantify your modeling impact clearly
Hiring managers want to see what your models actually moved. Replace vague claims with specifics: latency reduced, prediction accuracy improved, or revenue attributed to a deployed system. Concrete outcomes separate senior candidates from mid-level ones faster than any credential.
Tailor your resume to the stack
Senior data science engineer roles vary widely by tooling. One company runs Spark on Databricks, another on Kubernetes with Ray. Scan each job posting for the exact stack and mirror that language in your resume so automated screening and hiring managers both register the match.
Target roles by architecture ownership
Some postings want someone to build pipelines, others want someone to own the full ML platform. Read the responsibilities section carefully and apply to roles where the scope matches what you can lead independently, not just contribute to.
Apply early to roles that fit
Migrate Mate lists senior data science engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a system design walkthrough
Most senior-level loops include a machine learning system design round. Practice walking through feature stores, serving infrastructure, monitoring, and retraining pipelines out loud. Interviewers are testing whether you think end-to-end, not just whether you can tune a model.
Negotiate scope before you negotiate compensation
At this seniority level, title scope and team ownership vary more than the pay band. Before your final conversation, clarify what decisions you'd own, whether you'd manage engineers, and how success gets measured. Misaligned scope costs more than a lower offer over time.
Senior Data Science Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most senior data science engineers?
The companies hiring the most senior data science engineers right now include Amazon, Capital One, and Meta, with the largest share of openings in California, New York, and Virginia, based on current listings on Migrate Mate as of August 2026. Demand is especially high at companies scaling ML infrastructure across cloud-native platforms.
How many senior data science engineer jobs are remote?
About 64% of senior data science engineer openings are fully remote or hybrid as of August 2026, making this one of the more flexible senior technical roles in the market. ML platform engineering and research-adjacent positions tend to carry the highest share of fully remote arrangements compared to roles tied to real-time production systems.
How do you become a senior data science engineer?
You reach the senior level by owning production machine learning systems end-to-end, not just building models in notebooks. That means shipping models that run reliably in production, learning pipeline orchestration and feature engineering at scale, and contributing to architectural decisions. Most engineers get there by taking on increasing infrastructure ownership over several years and demonstrating that their work directly influences business outcomes.
Can you get hired as a senior data science engineer with limited experience?
It's possible if you can demonstrate depth in a specific area that is hard to find. Strong open-source contributions, a portfolio of production ML projects, or deep expertise in a specialized domain like recommendation systems or computer vision can offset a shorter work history. Targeting startups or smaller teams where scope is broader and seniority expectations are more flexible also improves your chances significantly.
What does the senior data science engineer interview process look like?
The process typically includes a recruiter screen, a technical phone interview covering ML concepts and coding, a machine learning system design round, and a full loop with several panel interviews. The system design round is the stage where senior candidates are most frequently filtered out. Expect to walk through how you would architect a complete ML system, including data ingestion, training, serving, and monitoring, rather than just solve a modeling problem.
Where can I find and apply to senior data science engineer jobs?
You can find and apply to senior data science engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Find roles that match your experience and specialization, then apply directly to each listing.
See All 582+ Senior Data Science Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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