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Job Field: Office Jobs
Location: SEATTLE, WA
Salary: $-
Job Type: Part Time

JOB SUMMARY:

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               <td align="left"><font style="font-size: 100%;">Title: Senior Data Analyst<BR>Location: Washington-Seattle<BR>Other Locations: Washington-Seattle<BR>Nordstrom is a fashion specialty retailer founded on a simple idea: offer each customer the best possible service, quality, value and selection. We are looking for statistical analysis of personalization test initiatives, recommendation of engine output analysis, and provision of ongoing/timely analysis of personalization testing projects in both online and offline environments.<BR><BR>At Nordstrom, providing great customer service goes beyond the selling floor. It takes a team of individuals with a variety of talents and skills to continuously improve service and our results. At our Seattle Headquarters, we work in partnership with other areas of the Company to support our frontline salespeople and customers. When you join Nordstrom, you are welcomed into a family dedicated to creating memorable and rewarding shopping experiences for our customers.<BR><BR>This position is designed to help understand the details of Nordstrom?s customer behavior with respect to our brands, cross-brand purchasing, shopper behavior, department cross-shopping, and general in-store purchase behavior by leveraging our internal customer knowledge warehouses.  A key focus of the position involves analyzing high volume complex customer purchase data derived from a series of ongoing personalization test initiatives across multiple channels and platforms: online, in-store, email, survey, etc.<BR><BR>The ideal senior data analyst is motivated, results-oriented and committed to providing outstanding customer service everyday.<BR><BR>Responsibilities<BR>* Support key Personalization initiatives and processes to develop and optimize statistical approaches that model customer multi-channel purchase behavior with respect to seasonality, promotion, brand, and/or product in a retail environment<BR>* Analyze, interpret, and summarize findings that serve to optimize and continuously improve Personalization processes, algorithms, and performance objectives<BR>* Recommend and implement best practices, provide actionable insights, and identify business opportunities in a collaborative team environment<BR>* Apply knowledge of experimental designs to implement and analyze results from extensive Personalization tests in both online and in-store contexts (including recommendation engine results) using standard statistical tests<BR>Qualifications<BR>* Bachelor''s degree (advanced degree preferred) in statistics/applied statistics, machine learning, mathematics, econometrics, computer science, or other quantitative discipline<BR>* 3 years experience with SQL assembling large data sets from both structured and unstructured sources using data marts/warehouses such as Oracle and Teradata<BR>* Proven ability to implement automated, scalable, efficient, and reliable processes/programs to derive, transform, model, and score large data sets<BR>* Experienced formulating, implementing, and validating predictive models using linear/logistic regression, decision                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     </font></td>
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