Decision Scientist

Spectraforce Technologies; Seattle (WA); On-site; USD 90,000 - 140,000; Full time

Job summary

Spectraforce Technologies is seeking a Decision Scientist to guide business decisions by applying research, statistics and modeling. The role emphasizes embedding analytics into business units and driving a data-driven culture across the organization from Seattle, WA.

Responsibilities include data prep from Azure/SQL/Oracle, building models, dashboards and storytelling presentations, and collaborating with stakeholders to operationalize insights.

Qualifications

• Proficiency with Excel, SQL, SAS, R, Python, Tableau and Power BI for analytics and reporting.
• Experience in data prep, modeling and data visualization across cloud and on-prem environments.
• Ability to communicate data insights clearly to non-technical stakeholders.

Responsibilities

• Data Preparation: extract and synthesize data from Azure Data Lake Storage, SQL Server, and Oracle; perform EDA and cleansing.
• Data Visualization: create standard and custom reports and dashboards; tell data stories.
• Data Manipulation & Modeling: pull large datasets from AWS, Azure and on-prem systems; build data models.

Skills

Excel

SQL

Python

R

SAS

Tableau

Power BI

Data storytelling

Statistics

Communication

Tools

Azure Data Lake Storage

SQL Server

Oracle

Azure Cloud

AWS

Tableau

Power BI

Job description

Job Title - Decision Scientist

Duration - 7+ Months

Location - Onsite, Seattle, WA

Job Description

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.

Summary of Key Responsibilities

• 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.