Design, develop, and implement advanced data science models and machine learning solutions for key business use cases across online experience, marketing, merchandising, and store operations. Apply advanced data science techniques to large datasets and collaborate with cross functional groups to derive actionable insights and develop scalable AI solutions. Execute best-in-class advanced analytics with scalable and reusable codebase and models that solve business problems. Generate and test hypotheses that align with priority use case. Comply with analytics standards, including guidelines for tailoring analytics methodologies to specific use case needs (e.g., ML, AI, descriptive analytics). Define internal or external, batch or real-time streaming data needs, evaluate data quality, and extract/manipulate data in a "Big Data" environment. Create compelling interactive visualizations and presentations to enhance decision making capabilities throughout the company. Work with Data Engineering team as required to implement data pipelines and tools to ensure efficient and seamless execution of analytics. Be agile in identifying data issues and taking action to remediate. Provide support for business users to leverage insights and provide high value impact. Maintain an up-to-date view on methods being used in the data science community. May telecommute. Requires a Master’s degree in Statistics, Computer Science, Business Analytics or related field and 2 years of experience as Data scientist or related occupation.Position requires: 1. Building data models and pipelines to process different types of large datasets. 2. Fluency with complex algorithms and analytics methodologies across multiple platforms and languages, including Google Cloud Platform. 3. SQL and Python. 4. Core analytical and statistical methods, including regression, segmentation/clustering, predictive modeling, time-series analysis, and machine learning techniques. 5. Visualization tools such as Tableau and Power BI. 6. Collaborating with business functions to deliver solutions that support decision making. 7. Analyzing data to identify deliverables, gaps, and inconsistencies. 8. Data management and data modeling. 9. Version control with Git using tools, including PyCharm and VS Code