Problem Framing & Strategy: Collaborate closely with Product and Engineering teams to define and frame high-impact problems, determining data requirements and designing robust data collection and annotation strategies.
Data Preparation & Feature Engineering: Process and analyze large volumes of unstructured text data, performing essential tasks such as tokenization, normalization, and advanced feature extraction.
Modeling & Innovation: Design, build, and rigorously evaluate cutting-edge AI architecture.
Experimentation & Validation: Execute comprehensive experiments (including A/B testing), define key evaluation metrics (like F1-score, ROUGE, or perplexity), and optimize model performance against business objectives.
Deployment Collaboration: Partner effectively with ML and Software Engineers to transition solutions from research prototypes to robust, scalable...
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