Senior Data Scientist, Supply Chain Data Science

Starbucks; Seattle (WA); Presencial; USD 135.000 - 220.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health benefits

401(k) matching

Stock program (Bean Stock)

Tuition coverage

Descripción de la vacante

Starbucks is seeking a Senior Data Scientist for its Seattle-based Supply Chain Data Science team. You will apply advanced analytics and machine learning to tackle complex supply chain problems, from problem framing to production delivery.

You will build end-to-end, production-ready data science solutions, including scalable pipelines and robust Python implementations, collaborating with technical and non-technical stakeholders to turn modeling work into actionable decisions.

Formación

• 4+ years of professional experience in data science, machine learning, applied analytics, or closely related field.
• BA/BS or advanced degree in a quantitative field such as computer science, data science, statistics, mathematics, engineering, or equivalent practical experience.
• Strong proficiency in Python and SQL.
• Experience developing production-level Python code with reliable, maintainable production solutions.
• Strong understanding of software engineering practices including modular code design, testing, version control, documentation, and code review.
• Experience with large, complex datasets and scalable data-processing solutions.

Responsabilidades

• Design, develop, validate, and deploy statistical and ML models for supply chain decision-making.
• Build reliable, scalable data and model pipelines integrated into business workflows.
• Apply software engineering practices: version control, testing, documentation, reproducibility.
• Communicate approaches, results, limitations, and recommendations to stakeholders.
• Lead complex data science projects from problem definition to production delivery.
• Mentor other data scientists through guidance and code reviews.

Conocimientos

Python

SQL

Data science

Machine learning

Communication

Educación

BSc in quantitative field

MS/PhD preferred

Herramientas

Spark

Databricks

Descripción del empleo

Starbucks is looking for a Senior Data Scientist to join its Supply Chain Data Science team in Seattle. In this role, you will apply advanced analytics and machine learning to tackle complex supply chain challenges, with responsibility spanning from early problem framing through production delivery.

The position focuses on building end-to-end data science solutions that are production-ready, including scalable data pipelines and well-engineered Python implementations. You will also collaborate with a mix of technical and non-technical stakeholders to turn modeling work into clear, actionable decisions.

What you’ll do

• Design, develop, validate, and deploy statistical and machine learning models that support supply chain decision-making, including analysis of complex supply chain data and translation into recommendations.
• Design, build, and own reliable, scalable data and model pipelines that integrate data science solutions into business and operational workflows.
• Use strong software engineering practices such as version control, code review, testing, documentation, and reproducibility.
• Communicate technical approaches, assumptions, results, limitations, and recommendations effectively to technical and non-technical stakeholders.
• Independently lead complex data science projects, including management of technical dependencies and risks, from problem definition through production delivery.
• Mentor and support other data scientists through technical guidance and code reviews.

Key skills and qualifications

• 4+ years of professional experience in data science, machine learning, applied analytics, or a closely related field.
• BA/BS or advanced degree in a quantitative field such as computer science, data science, statistics, mathematics, engineering, or equivalent practical experience.
• Strong proficiency in Python and SQL.
• Demonstrated experience developing production-level Python code, including converting prototypes into reliable and maintainable production solutions.
• Strong understanding of software engineering practices including modular code design, testing, debugging, version control, documentation, and code review.
• Experience working with large and complex datasets and building scalable data-processing solutions.

Technologies

• Python
• SQL
• Spark
• Databricks

Preferred qualifications

• MS or PhD in a quantitative discipline such as computer science, data science, statistics, operations research, engineering, mathematics, or a related field.
• Experience developing data science solutions in supply chain, forecasting, inventory, replenishment, or planning environments.
• Experience with Spark or other distributed data-processing technologies.
• Experience with modern cloud-based data and machine learning platforms, such as Databricks.
• Experience with CI/CD practices for data science or machine learning solutions.
• Experience monitoring production solutions and improving reliability and performance over time.
• Ability to mentor data scientists and raise engineering and coding standards within a data science team.

Compensation and location

• Location: Seattle, WA (onsite)
• Salary: USD 134,700 - 219,700 per year

Benefits

• Medical, dental, and vision
• Basic and supplemental life insurance
• Other voluntary insurance benefits
• Short-term and long-term disability
• Paid parental leave
• Family expansion reimbursement
• Paid vacation
• Sick time (accrued at 1 hour for every 25 hours worked)
• Eight paid holidays
• Two personal days per year
• 401(k) retirement plan with employer match
• Discounted company stock program (S.I.P.)
• Starbucks equity program (Bean Stock)
• Incentivized emergency savings
• Financial well-being tools
• 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
• Access to other educational opportunities
• Backup care
• DACA reimbursement