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Data Scientist, Merchandise Analytics
3 months ago
The Data Scientist, Merchandising Analytics at Tractor Supply Company will play a key role in leveraging data to address complex business challenges. The role will develop advanced statistical methods using Machine Learning, AI, statistical modeling, and optimization techniques to support the merchandising strategies and broader organizational goals of TSC. Additionally, the role will contribute to setting objectives to enhance the overall data architecture and data governance within the Merchandising team. Key areas of focus within Merchandising will be Space Planning and Pricing.
The Data Scientist will lead cross-functional projects, design and implement predictive models, and promote data-driven decision-making throughout the organization. Strong communication skills are essential as this role will translate complex analytical results into clear, actionable insights for both technical and non-technical stakeholders. This role work will ensure that Tractor Supply Company remains at the forefront of industry trends and emerging technologies in the data science field.
Essential Duties and Responsibilities (Min 5%)
Required Qualifications
Experience: 3-5 years proven experience as a Data Scientist, preferably in the retail or e-commerce industry. Prefer 2+ years of experience in predictive modeling utilizing CRM or transaction information.
Education: Bachelor’s Degree in Mathematics, Statistics, or Econometrics. Master’s Degree prefered. Any combination of education and experiencce will be considered.
Professional Certifications: None
Preferred knowledge, skills or abilities
Working Conditions Hybrid / Flexible working conditions Physical Requirements Sitting Standing (not walking) Walking Kneeling/Stooping/Bending Lifting up to 10 pounds Disclaimer
This job description represents an overview of the responsibilities for the above referenced position. It is not intended to represent a comprehensive list of responsibilities. A team member should perform all duties as assigned by his/ her supervisor.