Zumiez Inc. is seeking a Statistician - Retail Analytics to leverage statistical methods and data modeling techniques to drive strategic decisions across various business functions. The role involves developing statistical models, analyzing large datasets, and collaborating with cross-functional teams to translate insights into actionable business strategies.
Develop and maintain statistical models for forecasting and predictive analytics
Translate statistical results into clear, business-friendly insights
Analyze large statistical datasets to identify trends, patterns, correlations and opportunities in sales trends, product performance, logistics and other areas
Collaborate with cross-functional teams (e.g., Marketing, Merchandising, supply chain, Finance) to align statistical analysis with business goals
Create dashboards and reports to communicate findings to stakeholders in a clear and actionable manner
Ensure statistical data integrity and apply best practices in statistical analysis and data governance
Collaborate with data engineering teams to improve data collection and processing pipelines
Qualification
Required
Requires a Master's degree (or foreign equivalent) in Statistics, Computer Science, Data Science, or a related field plus 2 years of experience as a Data Scientist, Advisor – Data Analytics or related
Will accept a Bachelor's degree (or foreign equivalent) in Statistics, Computer Science, Data Science, or a related field plus 5 years of progressive post-baccalaureate experience as a Data Scientist, Advisor – Data Analytics or related in lieu of a Master's degree plus 2 years of experience
Must possess 2 years of experience with Master's or 5 years of experience with Bachelor's with: (a) conducting statistical analysis-based scripting with R, Python, SQL; (b) applying DAX for advanced data modeling, KPI development, and reporting; (c) performing data transformation, cleansing and manipulation using Python libraries (Pandas, NumPy, Matplotlib, Seaborn), R libraries (dplyr, tidyr, ggplot2, plotly), and AWS S3 and Glue for Scalable data processing; (d) conducting descriptive analysis to summarize historical data; (e) using predictive models to forecast future trends; (f) applying prescriptive analytics to recommend optimal actions and strategies using supervised and unsupervised machine learning algorithms, such as K-Nearest Neighbors, K-Means Clustering, Deep Learning, Time Series Analysis, and Self-Organizing Maps; (g) managing databases with Microsoft SQL Server, including writing and optimizing SQL queries for data retrieval, manipulation, and reporting; (h) automating reporting processes and improving efficiency using ETL and data warehousing methodologies; (i) preparing data with Power Query, creating visualizations and dashboards in Power BI; (j) scaling data storage and processing using data lake architectures; and (k) project Management using Jira, Confluence, Microsoft Planner and Azure Devops
