amplify - Associate Data Scientist
Requirements
• Bachelor’s degree in a quantitative field (e,g., Data Science, AI Engineering, Computer Science, Economics, Math, Physics, Statistics) or equivalent experience. • Proficient in using Python for data analysis tasks (data cleaning, manipulation, analysis). • Experience applying foundational statistical methods, such as regression and hypothesis testing. • Experience applying AI solutions to a variety of problem domains. • Experience with software development methodology and protocols, including version control and testing. • Demonstrated ability to independently research, troubleshoot, and solve open-ended analytical problems. • Excellent communication skills in writing and conversation, especially with non-technical partners. • Comfortable working independently and collaboratively in a team environment. • Experience training and evaluating the performance of machine learning models leveraging industry standard libraries like scikit-learn, xGBoost. • Familiarity with AI/ML frameworks and libraries such as PyTorch, TensorFlow, or Hugging Face. • Experience building or fine-tuning applications using Generative AI or Large Language Models. • Background in education or educational technology (EdTech)
Responsibilities
• As an Associate Data Scientist in the Product Spoke, you will analyze large-scale educational data to derive actionable insights that drive product design and improve customer experience. You will collaborate with cross-functional teams to tackle complex questions, leveraging both traditional analytical methods and emerging AI-driven workflows. • Partner with product managers, researchers, engineers, and content developers to answer complex business and educational questions. • Develop, evaluate, and maintain statistical models and AI-enabled analytical solutions.. • Communicate findings and recommendations to technical and non-technical stakeholders. • Leverage AI-powered tools to improve exploratory analysis, feature engineering, and model development while maintaining high standards for code quality and evaluation.
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