Advanced Analyst II

Meijer•Grand Rapids, MI

About The Position

This experienced role is part of Meijer’s Data Science & Analytics department. The department combines technical expertise in statistics and machine learning with communication and collaboration to support business areas across the enterprise. The Analyst develops and applies advanced analytics and machine learning solutions, collaborates proactively with business partners, and anticipates business needs. The work supports data-driven actions intended to improve sales, margin, and profit performance and enrich the lives of the communities Meijer serves. The role delivers analytics solutions across areas including merchandising, marketing, supply chain, product flow, operations, and real estate. It partners with stakeholders to develop insights and recommendations, leads analytics initiatives, and promotes adoption of advanced analytics. The Analyst develops custom algorithms, statistical models, machine learning and artificial intelligence solutions, scorecards, and actionable recommendations using data from disparate sources. The role also defines and analyzes key performance indicators and communicates technical findings through data storytelling, presentations, email, dashboards, and reporting. The customer is kept at the center of the analytic approach, analysis, and solutions. Other duties may be assigned or required.

Requirements

Bachelor’s degree in Business, Statistics, Economics, or another quantitative field, or relevant experience.
At least 2 years of relevant analytics experience in an applied role.
Demonstrated strengths using several of the listed analytics, data science, programming, cloud, and reporting tools and platforms.
Experience with Azure Cloud technologies, including Azure DevOps, Azure Synapse, MLOps, and GitHub.
Experience developing enterprise reporting, dashboards, and semantic models using Microsoft Fabric, Power BI, DAX, and Tabular Editor.
Experience working with large datasets from disparate sources and developing statistical models and/or ML/AI systems, such as natural language processing, speech/text/image recognition, supervised and unsupervised learning models, spatial modeling, forecasting, or econometric time-series models.
Experience working in cross-functional teams.
Strong work ethic; proactive and action-oriented.
Excellent listening, verbal, and written communication skills.
Ability to collaborate with and present to internal and external partners, including leadership.
Strong organizational, planning, and time-management skills, including the ability to prioritize multiple or competing projects and assignments.
Strong curiosity toward problem solving.

Nice To Haves

Master’s degree.
At least 2 years of relevant applied analytics experience in retail, logistics, supply chain, or CPG.
Safe Agile, Azure, Databricks, or Python certifications.
Experience in business or financial analysis, promotional analytics, customer analytics, pricing, merchandising, media analytics, corporate real estate analytics, pharmacy, or related areas.
Multi-channel experience and familiarity with both in-store/branch and digital analytics.

Responsibilities

Deliver data-driven solutions across merchandising, marketing, supply chain, product flow, operations, real estate, and other business areas.
Partner with business stakeholders to understand how data-driven insights and recommendations can improve business outcomes.
Anticipate future business needs, identify opportunities with substantial organizational impact, and lead the development of analytics initiatives.
Champion adoption of advanced analytics capabilities and recommendations across the business.
Build and maintain relationships with industry partners, including suppliers, vendors, and CPGs, to enhance analytics capabilities, knowledge, and impact.
Stay informed about retail-sector developments and use that information to generate proactive insights and recommendations.
Develop custom algorithms, statistical models, machine learning solutions, and artificial intelligence solutions to provide tailored insights and recommendations.
Develop scorecards and partner with reporting teams to improve reporting effectiveness.
Leverage disparate data sources and platforms to solve complex business problems.
Develop actionable recommendations for areas such as marketing, real estate, and merchandising.
Define and analyze key performance indicators to monitor data-product health and measure value.
Communicate technical concepts and results through data storytelling, in-person and virtual presentations, email, dashboards, and reporting.
Drive adoption and use of analytical insights, data products, models, and recommendations.
Keep the customer central to analytics and partner with cross-functional teams to promote customer-centered focus.