Full Job Description
The Marketing Analytics team is responsible for advanced analytics, segmentation & predictive modeling at The Home Depot. The team supports primarily Digital Marketing & Marketing Technology initiatives. Online Personalization, Merchandising and Traditional Marketing are also under the purview of Marketing Analytics team.
The Manager, Data Science is responsible for implementing and managing big data platforms, advanced data science tools, techniques and processes to support The Home Depot’s customer centric marketing initiatives. This position is also responsible for providing actionable insights to a broad range of audience including senior leaders and internal clients from marketing, operations and merchandising. Being a subject matter expert in advanced statistical, econometric, and quantitative analytic techniques Manager, Data Science plays a pivotal role in enhancing THD’s ability to anticipate and fulfil our customer needs.
The Manager, Data Science is responsible for developing statistical models, implementing Machine Learning, creating of test/learn strategies, managing audiences segments, and measurement. Being a stakeholder in The Home Depot’s Marketing Technology & Innovation stack, this individual will act as a visionary liaison among Home Depot IT, Data Science, campaign management and vendor teams. Manager, Data Science is expected to be an entrepreneurial and inspiring leader who can keep customer first while driving towards THDs long term goals.
MAJOR TASKS, RESPONSIBILITES AND KEY ACCOUNTABILITIES
30% Project Identification,Scoping & management: Partner w/marketing & merchandising leadership to identify business needs for customer analytics. Provide thought partnership in strategic merchandising & marketing decisions. Ensure accurate & timely completion of various projects by effectively utilizing offshore & onsite resources.
20% Establish best practices and identify opportunities to improve efficiency. Develop best in class methodologies to extract actionable insights from new data streams like mobile app, social and clickstream data.
10% Mentor new team members and provide Subject Matter Expertise in Statistical analytic techniques.
10% Develop Predictive models to identify marketing opportunities.
20% Customer Analytics: Provide analytical and customer-centric decision support to Marketing & Merchandising teams. Leverage customer database and other information systems to identify, integrate and analyze data.
10% Dashboard Development and Reporting: Establish and report on metrics of assigned projects or categories to gauge business impact and opportunities for improvement.
NATURE AND SCOPE
Position reports to Sr Manager Customer Analytics
No direct reports
ENVIRONMENTAL JOB REQUIREMENTS
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.
Typically requires overnight travel less than 10% of the time.
Additional Environmental Job Requirements:
Must be eighteen years of age or older.
Must be legally permitted to work in the United States.
Additional Minimum Qualifications:
The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job.
Years of Relevant Work Experience: 5 years
Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.
Master’s degree in Statistics, Industrial Engineering, Operations, or equivalent
Digital marketing experience or advertising operations
Previous experience in predictive modeling/ machine learning
Previous experience in leading large analytical projects
Skilled in Python, R, Scala, Spark, Hive, Map Reduce, SAS, SQL
Knowledge, Skills, Abilities and Competencies:
Proven ability to analyze, evaluate and interpret complex data using advanced statistical, econometric and quantitative analysis techniques.
Expertise in quantitative theories and techniques (regression, decision trees, logistic regression, cluster/factor analysis and ANOVA etc) and their role in a business context.
Skilled in the use of statistical programming software, especially SAS or R.
Proficient with PowerPoint, Excel, Access.
Proficiency in working with large relational databases, especially Teradata, Netezza and Hadoop.