Decision Scientist: Market Strategy & Analytics

Starbucks Corp.

Seattle (WA)

On-site

USD 110,000 - 160,000

Full time

2 days ago
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Benefits offered by this job

Health insurance
Dental insurance
Vision insurance
Life insurance
Disability
Parental leave
Paid vacation
Paid holidays
401(k) match
Bean Stock equity program
Tuition assistance
Backup care
DACA reimbursement

Job summary

Starbucks Coffee Company in Seattle is seeking a decision scientist to join Market Planning and Business Systems. You will analyze complex datasets, build predictive models, and translate insights into actionable strategies for Coffeehouse locations.

Collaborate with Finance, Operations, Data Science, and Data Engineering to shape long‑term planning, capital deployment, and investment scenarios. Strong communication to non‑technical audiences is essential.

Qualifications

  • BA/BS or equivalent with focus in statistics, math, CS, econ, or quantitative social science.
  • 1+ year of experience performing data analysis and profiling using SQL and Python or R.
  • Experience with data visualization tools (Tableau, Power BI, Shiny, D3).
  • Bonus: experience with geospatial data for predictive modeling.

Responsibilities

  • Analyze and model data from multiple sources to evaluate Coffeehouse Locations.
  • Generate insights and communicate implications to non‑technical audiences.
  • Collaborate cross‑functionally with Coffeehouse Development, Finance, Operations, Data Science, and Data Engineering.
  • Support model development and validation with senior practitioners.

Skills

SQL
Python
R
Data analysis
Communication
Team collaboration

Education

BA/BS in Statistics, Math, CS, Econ or related

Tools

Databricks
AWS
Azure
Tableau
Power BI
Alteryx
GIS

Job description

Starbucks Coffee Company in Seattle is seeking a decision scientist to join Market Planning and Business Systems. You will analyze complex datasets, build predictive models, and translate insights into actionable strategies for Coffeehouse locations.

Collaborate with Finance, Operations, Data Science, and Data Engineering to shape long‑term planning, capital deployment, and investment scenarios. Strong communication to non‑technical audiences is essential.

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