Decision Scientist

Benefits: Competitive salary Opportunity for advancement Training & development Decision Scientist About the Role The Decision Scientist will partner with Digital Product Managers, Engineering, UX, Operations, and Analytics teams to drive data-informed decisions across digital ordering experiences. This role combines advanced analytics, experimentation, forecasting, AI-enabled insights, and business performance analysis to improve digital experiences, operational efficiency, and product outcomes. The ideal candidate brings strong analytical expertise, business acumen, and the ability to translate complex findings into clear recommendations for product leaders and senior executives. Benefits and Opportunities Influence product strategy, roadmap priorities, and investment decisions Work with large-scale data across cloud and on-premises environments Build AI-enabled analytics tools and self-service reporting capabilities Lead experimentation, forecasting, and product measurement initiatives Collaborate with cross-functional product, engineering, UX, and operations teams Core Responsibilities Product Analytics and Decision Support Analyze product, operational, transaction, and digital experience performance to identify trends, risks, root causes, and opportunities. Develop recommendations that influence product prioritization, roadmap planning, feature optimization, and investment decisions. Build analytical models, forecasts, scenario-planning tools, and opportunity-sizing assessments. Define key performance indicators and monitor product, operational, and business outcomes. Quantify business impact and measure return on investment for product initiatives. Experimentation and Product Measurement Define measurement strategies and success criteria for new products, features, and digital experiences. Design and evaluate A/B tests, pilots, and experiments. Measure adoption, engagement, conversion, transaction success, operational efficiency, and feature utilization. Create standardized product measurement frameworks across platforms and channels. Evaluate pilot results and provide recommendations for broader implementation. Dashboards, Data Products, and AI Enablement Build and maintain product health scorecards, performance dashboards, and automated reporting solutions. Develop AI-enabled and self-service analytics tools for product teams. Automate recurring analysis, monitoring, and reporting activities. Partner with data engineering and analytics teams to improve data quality, accessibility, and reporting capabilities. Enhance existing dashboards and data products based on evolving business needs. Business Problem-Solving and Communication Lead analysis of complex and ambiguous business questions. Develop hypotheses, research approaches, measurement plans, and actionable recommendations. Translate technical analysis into clear business implications. Create executive-ready presentations covering performance, risks, opportunities, and recommended actions. Present findings to product leadership and senior executives. Promote best practices in decision science, experimentation, product analytics, and AI-enabled reporting. Required Qualifications Bachelor's degree in analytics, data science, statistics, economics, business, computer science, or a related field preferred. At least four years of experience in strategic analytics, product analytics, or decision support. At least five years of experience communicating analytical findings and producing detail-oriented deliverables. Advanced proficiency in SQL, Excel, Python, R, SAS, Tableau, or Power BI. Experience with Azure Data Lake Storage, Azure SQL Server, Oracle, AWS, on-premises systems, and web-based data sources. Experience performing exploratory data analysis, data cleansing, transformation, aggregation, and large-scale data manipulation. Knowledge of experimental design, A/B testing, forecasting, and scenario planning. Ability to explain complex technical findings to non-technical audiences. Strong business acumen and understanding of operational and digital product processes. Technology Azure Oracle SQL Server Python, R, and SAS Tableau or Power BI Microsoft Office Suite Smartsheet Preferred: Experience with Databricks. Key Success Measures Success may be measured through digital experience performance, order-entry speed, error rates, adoption, engagement, cart completion, checkout success, payment speed, transaction reliability, order accuracy, throughput, peak-hour performance, feature utilization, and satisfaction indicators. #J-18808-Ljbffr