Data Scientist

Amplify helps teachers bring delight and rigor to students every day. We have become a leader in K–12 literacy, biliteracy, math, and science by building inspiring teaching and learning experiences based on research. The Amplify Classroom platform combines curriculum, assessment, and supplemental learning into one coherent high-quality instructional system. A pioneer in education since 2000, Amplify has developed deep relationships in states and districts by partnering with educators to drive implementation quality and improved outcomes. Today, Amplify serves more than 18 million students and teachers across all 50 states and on six continents.

• 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.

• 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.

• Fully remote position.
• Equal Opportunity Employer.