Data Scientist, Amplify

• 2+ years of experience in a data science role working on Supply Chain forecasting or logistics optimization problems
• Proficiency with statistical methods and especially time series forecasting methodologies (eg. SARIMA, prophet, xGBoost)
• Expert user of Python for data analysis tasks (data cleaning, manipulation, analysis)
• Expert user of SQL, especially its use in data analysis tasks
• Experience training and evaluating the performance of machine learning models leveraging industry standard libraries like PyTorch, scikit-learn, tidymodels, xGBoost
• Experience deploying machine learning models from research environments into production environments like AWS Sagemaker, Databricks or Snowpark ML
• Demonstrated application of software development methodology and protocols, including using git for version control and testing
• Excellent communication skills in writing and conversation, especially with non-technical partners
• Experience driving self-directed projects and working cross-functionally

Desirable

• We seek an experienced Data Scientist to join our Supply Chain Analytics team, developing Machine Learning models to forecast demand, optimize inventory, and reduce costs across our educational product portfolio
• This role will drive data science best practices and introduce advanced analytics capabilities to support strategic supply chain decisions affecting tens-of-thousands of ISBNs and hundreds of millions in revenue every year
• To accomplish this, the Data Scientist will work with a cross functional scrum team of Analytics Engineers and Data Analysts to build state of the art data products
• This Data Science role will report to our Data Science manager who leads Data Science for Amplify’s Sales, Marketing and Finance departments
• As such, they will be empowered to innovate and participate in important decisions across the entire data stack
• Train, test, and deploy Machine Learning models: Drive the development of new Machine Learning capabilities by contributing at every stage of the Machine Learning delivery pipeline including research, evaluation, deployment and monitoring
• Seek the why behind every observation: Use advanced data analysis techniques to construct compelling narratives and recommend actions and strategies that our Supply Chain team can make to improve operations
• Work cross functionally to deliver superior data products: Contribute to development efforts from data ingestion, to data transformation, through to data analysis and machine learning in order to deliver high quality, impactful data products
• Give back to the broader Data Science team: Help our Data Science team continuously improve by leading team learning sessions and proposing novel tooling or architectures