Decision Scientist

Job Title
- Decision Scientist

Duration - 7+ Months

Location
- Onsite, Seattle, WA

This job contributes to Client's success by guiding business decisions through discovering and sharing insights that deliver stakeholder outcomes. You will apply research, statistics, experimental design, data synthesis, analysis, and develop models, dashboards, and storytelling presentations that support Client's global lines of business. The role requires a strong focus on embedding analytics into business units and fostering a data-driven culture across the organization.

Models and acts in accordance with Client's guiding principles.

• Data Preparation: Under the guidance of senior Decision Scientists, extract and synthesize data from various databases, including Azure Data Lake Storage, SQL Server, and legacy systems such as Oracle. Perform exploratory data analysis, cleanse, transform, and aggregate data. Process first-, second-, and third-party customer data within a next-generation, privacy-compliant infrastructure.
• Data Visualization: Work independently or under consultative direction to assess and create standard or custom reports, charts, graphs, and tables from structured data sources by querying data repositories. Demonstrate strong data storytelling and presentation skills.
• Data Manipulation & Modeling: Procure and manipulate large-scale, complex datasets from multiple platforms (AWS, Azure, Oracle, on-premises systems, web tools, etc.). Reshape data for analysis, filter data, remove outliers, and develop data models. Experience with cloud-based analytics solutions such as Azure and AWS is required.
• Data Methodologies & Analysis: Apply statistical analysis and modeling techniques, including descriptive and predictive analytics (e.g., regression, clustering, factor analysis, survival analysis, driver analysis, anomaly detection), dashboard development, and experimental design. Demonstrate proficiency with Excel, SQL, SAS, R, Python, Tableau, and/or Power BI while maintaining the confidentiality of sensitive data.
• Business Understanding: Develop a strong understanding of Starbucks' business and general business acumen. Initially work with guidance from senior team members while progressively strengthening the ability to connect data insights with business outcomes.
• Insights Operationalization: Present complex technical concepts to non-technical audiences in a clear and approachable manner. Support the implementation of analytics by documenting code, data transformations, algorithms, and embedding insights into business processes.
• Collaboration & Evangelism: Demonstrate outstanding collaboration skills across all organizational levels and promote a data-driven culture by partnering effectively with business and technical stakeholders.