Sr Data Scientist, Merchandising Analytics

Sr Data Scientist, Merchandising Analytics

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.

Data Science & Advanced Analytics:

• 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.

Business & Strategic

Collaboration:

• 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.

Data Engineering & Automation:

• 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.

Leadership & Team Development:

• 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.

Technical Excellence & Problem Solving:

• 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.

Experience:

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.

Education:

Bachelor’s degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Computer Science, or a related field required. Master’s degree in a quantitative discipline (Mathematics, Statistics, Operations Research, AI/Machine Learning, or a related field) preferred. Any combination of education and experience will be…