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.

Ready to make an impact? Apply now and join us on our journey!