Data Scientist, II

Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions for client services and product enhancement.

• Interacts with product and service teams to identify questions and issues for data analysis and experiments
• Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large data sets from multiple disparate sources
• Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers
• Create analytical models with emphasis on predicative and prescriptive analytics
• Support creation of visual graphics to adequately analyze historical data and display trending
• Support evaluation of 3rd party analytics algorithms and analytics framework
• Support Integration of statistical models into Zebra’s platforms, products or services by collaborating with software development team
• Perform data analysis of customer specific data to help diagnosis customer specific issues
• Evaluate and make recommendations to evolve data collected to improve analytics
• Evaluate and define new data to collect for products or services under consideration for development or in development cycle
• Meet with customers and present data analysis and reports; also gather customer requirements relative to data analytics, data modeling, data visualization

• Bachelor’s degree in Statistics, Mathematics, Data/Computer Science, or a related discipline. An advanced degree (Masters or PhD) is strongly preferred.
• Experience in supply chain and/or retail analytics and optimization. Proven application of time series forecasting techniques to solve real-world business problems within these domains
• 3-5 years of professional experience in data science, with a proven track record of deploying machine learning products into production. Experience in supply chain and/or retail analytics is a significant plus.
• Deep working knowledge of common machine learning and deep learning techniques, including regression, clustering, time series forecasting, and ensemble methods (e.g., Random Forests, Boosting).
• Strong programming skills in Python and SQL. Experience with core machine learning packages such as scikit-learn and PyTorch would be a plus
• Hands-on experience with the Azure cloud platform, particularly Azure Databricks. Proficient in large-scale data processing using PySpark and querying with Databricks SQL.
• Demonstrable experience building and deploying end-to-end ML solutions. Familiarity with CI/CD principles and tools like GitHub Actions, and experience with automated deployment frameworks such as Databricks Asset Bundles.
• Possesses a strong aptitude for, and enjoys, independently researching and solving complex, ambiguous problems.
• Excellent verbal and written communication skills, with the ability to clearly document code and articulate complex technical concepts to diverse audiences.
• Must be proficient in using visualization to identify patterns, anomalies, and insights from large, complex datasets 
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