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Date: Jun 8, 2017

Location: San Francisco, CA, US

Company: Williams-Sonoma Inc.

Requisition Number: SS-10939
Area of Interest: Information Technology
Organization: Corporate
Brand/Division: Shared Services
Position Type: Full-time

JOB DESCRIPTION

Our Company

Founded in 1956, Williams-Sonoma, Inc. is the premier specialty retailer of high-quality products for the kitchen and home in the United States. Our family of brands are Williams-Sonoma, Pottery Barn, Pottery Barn Kids, PBteen, West Elm, Williams-Sonoma Home, Rejuvenation, and Mark and Graham. These brands are among the best known and most respected in the industry. We offer beautifully-designed, stylish and functional products for every area of the home, including the kitchen, living room, bedroom, home office, closet, laundry room and even outdoor spaces. We've seen some big changes since our first brick-and-mortar store opened more than half of a century ago. What hasn't changed is our passion for high-quality products, functional design, outstanding customer service, and enhancing the lives of our customers and the communities where we operate. Today, we're a multi-brand, multi-channel, global enterprise supported by state-of-the-art technology and some of the most talented teams in retailing - and we're always looking for new energy and ideas.

Position Summary

Williams-Sonoma Inc. is looking for a Lead Data Science Engineer to join our Marketing Strategy & Analytics team. As a valued member, you'll deliver cross-channel, data-driven marketing assets yielding valuable insights and measurement resolution to the Williams-Sonoma, Inc. portfolio of brands. The team drives results in a variety of ways, including creating targeted marketing assets, delivering marketing-mix optimization strategy, predictive response modeling for push marketing channels, search optimization, sentiment analysis, analytic support to Store fleet, customer metrics and retention reporting, cross-channel test design, and in-depth analysis of customer behavior across sales channels, devices and customer touch points.

You'll be among a dynamic team of Data Scientists, gathering intelligence from large data stores across several relational and distributed file systems. Due to the volume of data, MapReduce ML functions are used to supplement traditional computational methods when performing advanced analytic methods. Your specialized experience in scripting and automation will advance the infrastructure that enables these solutions. You will participate in the full lifecycle of a project, from articulating business questions to scripting production-level code.

The work environment is friendly, supportive, deadline and ROI driven, and service oriented. We value Engineers with strengths in critical thinking and execution, but also embrace ambiguity and exhibit an equally irrepressible compulsion to disentangle complex quantitative business problems.

Specific Responsibilities

* Mastery of the data models across Teradata, Hadoop, Unix, and SAS platforms
* Translate business opportunities into analytic deliverables that drive actionable insights and have direct bottom line impact on topics such as:
* Multi-touch attribution / evaluate the ROI of marketing, with the goal of helping the organization understand the best way to optimize spend against key marketing objectives
* Build ETL to house externally and internally derived data sources across Teradata, TD Aster, Hadoop, SAS environments
* Partner with IT, Marketing and Vendor teams to ensure that accurate and comprehensive tracking and delivery solutions are in place
* Build behavioral-based targeting solutions for outbound marketing deployments, such as CLV or merchandise-driven style affinity
* Create solutions that optimize site conversion, such as on-site search performance, content to product recommendations, and other site improvements
* Communicate findings to cross-functional teams (creative, merchants, finance, marketing, etc.) to drive decisions and action.
* Production-quality scripting that delivers personalized marketing experiences to the customer at scale
* Author application code as well as shell scripting to automate execution in run-time environments.
* Technical mentorship on best-practices in efficient computing, data management, and toolsets.

Desired Exposure to Machine Learning:

* Predictive models (Linear, Logistic, Proportional Hazards, Boosted Regression Trees)
* Clustering (K-means, Fuzzy, C-means, Hierarchical, Mixture modeling)
* Classification (Decision Trees, Logistic, Deep Learning, SVM, Random Forest)
* Natural language processing
* Sentiment analysis

REQUIREMENTS AND QUALIFICATIONS

REQUIREMENTS AND QUALIFICATIONS

* Have passion for data and overwhelming curiosity to learn.
* Masters/Advanced degree in Statistics, Mathematics, Operations Research, Computer Science or other quantitative field with 2-5 years of industry experience.
* OR Bachelors degree in above mentioned quantitative field with 3-6 years of industry experience working directly with large and diverse data assets, data analytics and related research.
* Experience in data warehousing including dimensional modeling concepts.
* Develop strategies to extract, resolve, and unify information of various types from numerous disparate data sources (structured and unstructured).
* Maintain, enhance, and optimize the data pipeline for scalability and reliability
* SQL skills (self rated scale of minimum 8 out of 10)
* R, Python (self rated scale of minimum 5 out of 10)
* Shell scripting (self rated scale of minimum 8 out of 10)
* Appetite to work across multiple platforms, such as Teradata, Unix, Linux, Hadoop/Hive, SAS.
* Priority consideration will be given to the candidate having experience with Machine Learning techniques for classification, regression, clustering, and topic modeling.
* Familiarity with Adobe Analytics is a plus.
* Retail experience is a plus
* Strong written and verbal communication skills.
*LI-AB

Williams-Sonoma, Inc. is an Equal Opportunity Employer.

Williams-Sonoma, Inc. will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.


Nearest Major Market: San Francisco
Nearest Secondary Market: Oakland

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