Data Scientist

Lowes•Charlotte, NC

2d•Onsite

About The Position

The primary purpose of the Data Scientist is to provide analytical capabilities to support data science initiatives. This position solves complex problems with close supervision and direction of senior colleagues and is responsible for the data science support of various company initiatives. Finally, this position supports the development of automated self-service products and solutions for business partners to effectively interpret data and build data products.

Requirements

Bachelor’s degree in mathematics, statistics, physics, economics, engineering, computer science, data, information science, or related quantitative analytic field or equivalent years of experience in lieu of education requirement, if applicable
2 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting (fewer years may be accepted with a master’s or doctorate degree)
1 year of SQL and programming experience (fewer years may be accepted with a master's or doctorate degree)

Nice To Haves

Master’s degree in mathematics, statistics, physics, economics, engineering, computer science, data, information science, or related quantitative analytic field
1 year of experience using multiple data systems and sources
1 year of experience working with cross-functional partners
Exposure to forecasting or demand planning use cases (retail, supply chain, or e-commerce preferred).
Understanding of time series forecasting methods such as ARIMA, Prophet, or regression-based approaches.
Basic knowledge of machine learning models such as XGBoost, Random Forest, or GLM.
Exposure to big data tools such as Spark, PySpark, or cloud platforms (GCP/AWS/Azure).
Familiarity with ML workflow tools such as MLflow, Vertex AI, or similar (nice to have).
Understanding of forecast evaluation metrics such as MAPE, MAE, or RMSE.
Experience working in collaborative environments with cross-functional teams.

Responsibilities

Develop and maintain forecasting and machine learning models to support demand planning, inventory optimization, and business forecasting use cases.
Assist in building item-location level demand forecasting models using time series, machine learning, and statistical approaches.
Support development of forecasting solutions for promotions, seasonality, and trend-based demand changes.
Contribute to modeling promotion impact on demand, including basic uplift estimation and feature engineering for promotional signals.
Work with senior data scientists to apply advanced time series techniques such as ARIMA, Prophet, and ML-based forecasting models.
Support development of hierarchical forecasting approaches (top-down / bottom-up) under guidance of senior team members.
Extract, clean, and prepare large-scale datasets for modeling and analysis.
Collaborate with cross-functional teams including analysts, engineers, and product stakeholders to translate business requirements into data science solutions.
Assist in identifying drivers of forecast variance and improving forecast accuracy through model tuning and performance analysis.
Support deployment and monitoring of ML models in production environments.
Help build scalable and automated forecasting pipelines using cloud-based tools and big data frameworks.
Stay updated with emerging machine learning and forecasting techniques and apply them under guidance.