Sr Data Scientist, Merchandising Analytics

Tractor Supply Company•Brentwood, TN

3d•Onsite

About The Position

The Senior Data Scientist will be responsible for modeling complex business problems and initiating the development of advanced statistical methods through Machine Learning/ AI, statistical modeling, and optimization to support the broader Merchandising organization and TSC. In addition to statistical modeling, this position will be tasked with setting goals to drive the overall data architecture and data governance for Merchandising. This position will lead cross-functional projects, designing and implementing predictive models, and driving overall effective data-driven decision-making. Strong communication skills are required to effectively convey findings to both technical and non-technical stakeholders. This position will own the translation of predictive modeling results to actionable insights that can be utilized across the TSC organization, ensuring the company stays abreast of industry trends and emerging technologies in the Data Science field.

Requirements

5+ years of experience in predictive modeling, data science, advanced analytics, utilizing CRM or high-volume transaction data to drive business insights.
Strong understanding of Retail, Consumer Packaged Goods (CPG), or Marketing Analytics, with the ability to translate business challenges into data-driven solutions.
Proven track record of leading data-driven projects from definition to execution, influencing roadmaps, and providing strategic insights.
Experience managing structured and unstructured data, applying statistical techniques, and delivering actionable recommendations.
Bachelor’s degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Computer Science, or a related field required.
Any combination of education and experience will be considered.

Nice To Haves

Master’s degree in a quantitative discipline (Mathematics, Statistics, Operations Research, AI/Machine Learning, or a related field) preferred.
Intermediate / advanced proficiency in one or more programming languages: Python, PySpark, R.
Expertise in developing predictive models, statistical analyses, machine learning algorithms, and optimization techniques.
Deep experience in writing, debugging, and optimizing complex SQL queries for large-scale data manipulation and extraction.
Strong knowledge of A/B testing methodologies, including experimental design, execution, and post-hoc analysis.
Experience working with Azure, AWS, or other cloud computing platforms for model deployment and scalable data processing.
Familiarity with data engineering principles, including data transformation, ETL processes, and database optimization.
Proficiency with version control systems like Git for managing analytical workflows and reproducibility.
Hands-on experience with data visualization tools such as Power BI, Tableau, or similar platforms for delivering insights to business stakeholders.
Ability to effectively communicate complex analytical findings to senior and executive leadership, ensuring data-driven decision-making at all levels.
Strategic problem-solving skills, with the ability to frame business questions and develop analytical solutions that align with company objectives.
Strong verbal and written communication skills, with the ability to translate technical findings into actionable business recommendations.
Ability to manage multiple projects simultaneously, balancing short-term needs with long-term analytical initiatives.
Experience working cross-functionally with business teams, fostering collaboration, and influencing decision-making through data insights.
Fluent in English (spoken and written), with strong documentation and reporting abilities.

Responsibilities

Develop, maintain, and improve predictive models using Python, R, and Databricks to enhance business insights.
Extract and transform data from internal and external sources to build robust data science solutions that support key business decisions.
Design and implement A/B testing frameworks to evaluate the effectiveness of various business strategies, ensuring statistical rigor in experiment design and analysis.
Stay at the forefront of the AI and Machine Learning ecosystem, continuously evaluating and implementing emerging technologies to drive innovation.
Work closely with key business stakeholders to frame complex business questions, define objectives, KPIs, and deliverables, and ensure alignment of analytical solutions with strategic goals.
Translate predictive modeling outputs into actionable insights that drive tangible business impact across merchandising, pricing, and operational functions.
Foster a data-driven decision-making culture, collaborating with cross-functional teams and senior leadership to define the long-term vision for data science and engineering within the organization.
Identify opportunities for automation and optimization within data pipelines, modeling workflows, and analytics processes.
Work closely with data engineering and architecture teams to ensure scalable and efficient data structures, governance, and pipelines that support analytics initiatives.
Apply best practices in data management, version control, and model deployment to enhance reproducibility and efficiency.
Lead and mentor junior data scientists and analysts, providing guidance on modeling techniques, best practices, and strategic thinking.
Model company values and create a positive, high-performance environment that empowers the team to maximize business impact.
Manage multiple projects simultaneously with limited oversight, ensuring timely and high-quality delivery of data-driven solutions.
Serve as a go-to expert for analytical needs, including data extraction, trend analysis, statistical modeling, and machine learning applications.
Conduct code reviews, debug complex analytical problems, and assist in deployment to production environments.
Ensure analytical methodologies are rigorous, scalable, and interpretable, aligning with business goals and industry best practices.