Decision Scientist_26-00782

Job Title: Decision Scientist

Location: Seattle, WA (Hybrid)

Duration: 6 months with possible extension

Pay Range: $55/hr-$60/hr on W2

Job Description

• This Decision Scientist will partner with Digital Product Managers, Engineering, UX, and Operations to drive data-informed product decisions across Coffeehouse Ordering experiences. This role will leverage advanced analytics, experimentation, AI-enabled insights, and business performance analysis to help improve customer experience, operational efficiency, and product outcomes.
• The ideal candidate combines strong analytical capabilities with business acumen and the ability to translate complex data into actionable recommendations for product and business leaders.

Core Responsibilities

Product Performance & Insights

• Analyze product, operational, and customer experience performance to identify trends, root causes, opportunities, and risks.
• Develop actionable recommendations that influence product prioritization, roadmap decisions, and feature optimization.
• Anticipate stakeholder questions and proactively provide insights that support effective decision-making.
• Monitor and communicate performance against key business, customer, and operational KPIs.

Strategic Analytics & Decision Support

• Build models, analyses, forecasts, and scenario planning tools that inform strategic prioritization and investment decisions.
• Partner with Product Managers to quantify business impact, define success metrics, and measure return on investment for product initiatives.
• Support roadmap planning by assessing tradeoffs, sizing opportunities, and evaluating expected outcomes.

Experimentation & Product Measurement

• Define measurement strategies for new products and capabilities.
• Design and evaluate A/B tests, pilots, and experiments to validate hypotheses and guide product decisions.
• Establish product health, adoption, engagement, and operational success metrics.
• Create standardized measurement frameworks that can be applied consistently across products and channels.

Data Products, Dashboards & AI Enablement

• Build scalable dashboards, AI-powered tools, and self-service analytics capabilities that enable Product Managers to independently assess product performance.
• Identify opportunities to automate recurring analyses and reporting.
• Partner with data engineering and analytics teams to improve data quality, accessibility, and reporting capabilities.
• Drive enhancements to existing dashboards and data products based on evolving business needs.

Business Problem Solving

• Lead cross-functional teams through complex and ambiguous business questions by:
• Defining key problems and opportunities
• Developing analytical hypotheses
• Designing research and measurement approaches
• Synthesizing results into actionable recommendations
• Conduct inquiry-driven analysis to uncover emerging customer, operational, and business insights.

Executive Communication & Storytelling

• Develop concise, executive-ready narratives that communicate business performance, product outcomes, risks, and recommendations.
• Present findings and strategic insights to product leadership and senior executives.
• Translate technical analyses into clear business implications and recommended actions.

Thought Leadership

• Act as a trusted analytics partner across Coffeehouse Ordering and broader Digital Product teams.
• Promote best practices in product measurement, experimentation, decision science, and AI-enabled analytics.
• Bring an outside-in perspective on emerging analytics techniques, product measurement frameworks, and decision-support capabilities.

Daily Responsibilities:

Product Analytics & Monitoring

• Build and maintain product health scorecards and performance dashboards.
• Create automated reporting and monitoring solutions.
• Conduct recurring business reviews for product leaders.

AI & Self-Service Analytics

• Build AI-enabled tools that allow Product Managers to:
• Self-service product performance questions
• Explore trends and anomalies
• Access KPI reporting
• Generate insights and recommendations

Product Measurement & Experimentation

• Establish success criteria for new features and experiences.
• Measure feature adoption, conversion, efficiency improvements, and customer outcomes.
• Evaluate pilot performance and develop scaling recommendations.

Strategic Analysis

• Customer behavior analysis
• Operational efficiency analysis
• Forecasting and scenario planning
• Investment prioritization support

Interaction level with team:

• Moderate to high based on daily needs

Degree or certifications required:

• Degree in relevant field (BA)

Years experience:

• 4-5 years of experience, but the more the better, no max limit

Required background? Skills?

• Azure: data lake storage, SQL server and legacy systems
• Oracle; perform exploratory data analysis, cleanse, massage, and aggregate data.
• Proficiency in Excel, SQL, SAS, R, Python, Tableau / PowerBI, and experimental design platforms.
• Working knowledge and understanding of Starbucks business, and business acumen in general
• Provide analytic support (code documentation, data transformations, algorithms, etc.)
• Ability to procure and manipulate large-scale, complex data from a variety of systems (AWS, Azure, Oracle, on prem, web tool, etc.),
• Effectively presents complex technical material to non-technical audiences in an approachable way.

Nice-to-Haves:

• Data Bricks
• Former Starbucks

Top Candidate Skills & How Applied Years of Experience

1. Skill: Strategic Analytics & Decision Support 4

Application: to translate complex data into actionable recommendations

for product and business leaders.

1. Skill: Communication 5
2. Skill: Attention to details 5