Associate Data Scientist in Raleigh, NC at Advance Auto Parts

Date Posted: 9/3/2020

Career Snapshot

Career Description



Job Description

Join the AAP Data Science Team and start reimagining the future of automotive retail. Disrupt the way consumers buy auto parts and take on the industry’s biggest challengers to execute on AAP's top-down commitment to digital expansion. 

As a Data Analyst at Advance Auto Parts, you will have an opportunity to disrupt a $150B auto parts industry to bring better and faster solutions to customers. You will be part of creating an elite data science function helping the company live its mission of “Advancing a World in Motion”. The role will be part of a merit-based organization with a culture of professional growth and development, and emphasis on the latest tools, platforms and technologies. 

Essential Duties and Responsibilities include the following.  Other duties may be assigned. 

  • Identify and implement strategies leveraging marketing and non-marketing data across web, search, social, radio, email and others to drive company profit growth.  
  • Analyze customer, transaction, and operations data to find trends and patterns, create actionable insights, then help design and execute A|B, Multivariate, and DOE tests to prove your findings. 
  • Partner with data scientists to develop advanced regression, segmentation, and machine learning models to guide business decisions. 
  • Leverage the latest analytical tools and data mining techniques. 
  • Create presentations leveraging data visualization tools that clearly and concisely communicate analytical findings and recommendations to key stakeholders. 
  • Support ad hoc analytic and reporting needs as requested. 

Required Qualifications 

  • Bachelor’s in Computer Science, Mathematics, Engineering or other relevant field. 
  • Experience with a data programming languages (e.g. SQL, Python). 
  • Experience with statistics
  • Understanding of relational databases. 

Preferred Qualifications 

  • 1-2 years' experience in a quantitative analytics role. 
  • Experience with web analytics, including techniques like A/B testing, canary testing, and feature flags, and tools such as Google Analytics.
  • Experience creating reports and transitioning them to dashboards.
  • Experience with machine learning. 
  • Experience with data mining.