GOALS

• Build and deploy personalized recommendation algorithms to enhance user experience and increase platform engagement
• Optimize existing machine learning models for performance and scalability to handle the growing volume of user data
• Collaborate effectively with data engineers and scientists to streamline the development and deployment of AI models.

Frustrations

• Encountering challenges in integrating data from various sources and ensuring data quality for model training
• Facing difficulties in deploying trained models into production environments quickly and efficiently
• The steep learning curve associated with new AI technologies and tools, requiring significant time and effort for upskilling.

GOALS

• Develop and implement AI models for predictive diagnostics and personalized treatment recommendations in healthcare
• Ensure the privacy and security of sensitive patient data used in AI applications through robust data governance practices
• Stay up-to-date with the latest advancements in AI research and applications in the healthcare domain.

Frustrations

• Working with siloed and fragmented healthcare data, making it difficult to access and integrate for AI model training
• Facing challenges in deploying AI models within the complex regulatory landscape of the healthcare industry
• Bridging the gap between AI research and practical applications in real-world healthcare settings.