Director, Finance Decision Science & AI Strategy

Starbucks; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Medical, dental, vision

Life insurance

Disability coverage

Paid parental leave

Tuition assistance

401(k) retirement plan with employer 1

Job summary

Starbucks is seeking a Director of Decision Science to shape how Finance uses data as a strategic enterprise capability. You will lead a high-performing team of finance data scientists and analysts to enable advanced analytics, AI, and data-driven decision support across Finance.

You will establish the vision, governance, and modernization agenda, partnering with Finance, Starbucks Technology, and cross-functional teams to drive measurable business outcomes and ensure trusted data foundations

Qualifications

• BA/BS or equivalent experience.
• Experience with cloud data/analytics (Azure, AWS).
• Experience with Excel, SQL, Tableau/PowerBI, Python or R.
• Experience leading small teams (2–4) and strong communication.

Responsibilities

• Set the vision and strategy for Finance transforms decision support into an enterprise capability.
• Lead data modernization and architecture for advanced analytics, AI, and future-ready decision support.
• Establish governance and stewardship to improve data quality, accountability, and KPI usage.

Skills

Leadership

Data analytics

SQL

Python

R

Azure

AWS

Tableau/Power BI

Data governance

Team management

Education

BA/BS or equivalent experience

Tools

Azure

AWS

Tableau

Power BI

Job description

Starbucks is seeking a Director of Decision Science to shape how Finance uses data as a strategic enterprise capability. You will lead a high-performing team of finance data scientists and analysts to enable advanced analytics, AI, and data-driven decision support across Finance.

You will establish the vision, governance, and modernization agenda, partnering with Finance, Starbucks Technology, and cross-functional teams to drive measurable business outcomes and ensure trusted data foundations