Lead Analyst, Statistical Analysis

You will play a crucial role in developing and maintaining descriptive and predictive analytics models and tools to support Lowe's pricing strategy. Working closely with the Pricing team, you will be responsible for translating pricing goals and objectives into data and analytics requirements. By leveraging both open source and commercial data science tools, you will gather and analyze data to provide data-driven insights, trends, and identify anomalies. Applying various statistical and machine learning techniques, you will address relevant questions and offer retail recommendations. Collaboration with product and business teams will be essential to drive continuous improvement and maintain a competitive position in the pricing domain. Your core responsibilities will include translating pricing strategy and business objectives into analytics requirements, developing processes for collecting and enhancing large datasets, conducting data validation and analysis, designing and implementing statistical and machine learning models, ensuring the accuracy and reliability of model results, applying machine learning outcomes to business use cases, educating the pricing team on interpreting machine learning model results, designing and executing experiments to evaluate price changes, performing advanced statistical analyses, leading analytics projects, exploring new analytical techniques and tools, collaborating with cross-functional teams, promoting future-proof practices, staying updated with industry developments, and mentoring junior analysts. To excel in this role, you should possess 6-12 years of relevant experience in advance quantitative analysis, statistical modeling, and machine learning, along with a Bachelor's or Master's degree in Engineering, Business Analytics, Data Science, Statistics, Economics, or Mathematics. You must have expertise in Regression, Sampling techniques, hypothesis testing, Segmentation, Time Series Analysis, Multivariate Statistical Analysis, and Predictive Modelling. Additionally, you should have extensive experience in corporate Data Science, Analytics, Pricing & Promotions, Merchandising, or Revenue Management, as well as proficiency in SQL, Python, R, or SAS, enterprise-level databases, data visualization tools like Power BI and Tableau, and cloud platforms such as GCP, Azure, or AWS. Technical expertise in Alteryx and Knime would be desirable for this role.,