1. Custom Model Development and Product Support:

• Work closely with product managers and engineering teams to understand business challenges and design, develop, and implement custom machine learning models tailored to specific product features.

• Support the integration of finalized models into production systems, focusing on efficiency, scalability, and maintainability.

• Conduct rigorous exploratory data analysis (EDA) to inform model feature engineering and selection.

Documentation and Knowledge Transfer:

• Develop and maintain high-quality, comprehensive technical documentation for custom models, data pipelines, and product-specific data science methodologies.

• Create clear guides and artifacts for internal teams to ensure understanding and proper use of data science deliverables.

• Document modeling choices, data governance, and performance metrics for auditing and future iteration.

AI/LLM Technology Research and Implementation:

• Assist in the research, experimentation, and building of prototypes leveraging Generative AI and Large Language Models (LLMs).

• Support the team in tasks related to LLM fine-tuning, prompt engineering, and evaluating model performance for specific use cases (e.g., summarization, code generation, classification).

Reporting and Process Improvement:

• Design and implement dashboards or reports to monitor model performance and impact on key business metrics.

• Participate in code reviews, contributing to a culture of high-quality, reproducible data science practices.

• Present findings and model results clearly and effectively to technical and non-technical stakeholders.