Lead Data Analyst- Merchandising Analytics Essentials and Beauty

The pay range is $115,000.00 - $206,000.00

Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well-being and beyond at https://corporate.target.com/careers/benefits.

About us:

Target is an iconic brand, a Fortune 50 company and one of America’s leading retailers.

Behind one of the world’s best loved brands is a uniquely capable and brilliant team of data analysts. The Target Merchant Analytics team creates the tools and data products to sustainably educate and enable our business partners to make great data-based decisions at Target. We are experts in data sourcing, data visualizations, and providing action oriented insights used by clients throughout the enterprise. We also play a key role in identifying the test-and-measure or A/B test opportunities that continuously help Target improve the guest experience, whether they love to shop in stores or at Target.com.

A Lead Data Analyst role with our Merchandising Analytics team means you will 

• connect teams with trusted data and high-quality insights
• deliver world-class product solutions in partnership with our product teams
• driving prioritized metrics and efficient insights
• embrace a continuous-learning mindset 

As a part of the Merchandising Analytics team, our analysts work closely with business owners as well as technology and data product teams staffed with product owners and engineers. They support all Merchandising strategic initiatives with data, reporting and analysis.  Merchandising teams rely on this team of analysts to bring data to support decision making.

PRINCIPAL DUTIES AND RESPONSIBILITIES

As a Lead Data Analyst- Essentials and Beauty your responsibilities will be exploring data, technologies, and the application of mathematical techniques to derive business insights.  Data analysts spend their time determining the best approach to gather, model, manipulate, analyze and present data. 

You will be part of an agile, global team which requires active participation in ceremonies and team meetings.  You will also be part of a larger cross functional pricing team which will determine business, product/application and analytics priorities supporting pricing decisions.  Ad hoc analysis, dashboard creation, and providing insights that drive decisions are key outputs you will provide to the pricing analyst teams.  These outputs drive everyday pricing competitiveness, determine item elasticity, price band balance by category, and pricing parity for both online and in store assortments.   

In this role, you’ll have opportunities to continuously upskill to stay current with new technologies in the industry via formal training, peer training groups and self-directed education.  This role specifically will support a new initiative requiring new data and metric development.  Your curiosity and ability to roll up guest level insights will be critical.   This capability is constantly evolving, which allows for creative thinking and leadership opportunities. Job duties may change at any time due to business needs.

  

About you:

• Four-year degree or equivalent experience
• 6+ year as a Data Analyst with strong academic performance in a quantitative field; or strong equivalent experience [add any specific analyst experience needed here]
• Advanced SQL experience writing complex queries
• Accomplished with Python or R
• Solid problem solving, analytical skills, data curiosity, data mining, Data creation and consolidation
• Support conclusions with a clear, understandable story that leverages descriptive statistics, basic inferential statistics, and data visualizations Willingness to ask questions about business objectives and the measurement