Data Scientist to Build Forecasting Models for Retail Sales
Skills Required
Description
Retail sales are influenced by countless factors, and we need a data scientist who can help us make sense of those dynamics by building forecasting models. The purpose of this role is to provide insights that guide smarter business decisions and improve planning accuracy.
The models should account for seasonality, promotions, and external trends while still being flexible enough to adapt to shifting market conditions. This is not just about generating numbers—it’s about creating reliable forecasts that management can use with confidence.
Python will be the main language for this project, with Pandas and Scikit-learn forming the core of the modeling process. Strong SQL knowledge will also be essential since much of the data will be stored in relational databases.
Key responsibilities include:
Collecting and preparing large datasets from multiple sources.
Developing machine learning models to predict sales trends.
Testing model accuracy and refining based on performance.
Communicating results in a clear and actionable format.
Beyond technical skills, we want someone who can think about the business context. Forecasting is only useful when it ties back to practical strategies such as inventory planning and promotional timing.
Visualization will be an important component of the role. We want dashboards and reports that highlight results in ways that are accessible to non-technical stakeholders.
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