Leading cutting-edge research and development in applied machine learning, focusing on real-world applications of AI to drive business value and innovation.𝐊𝐞𝐲 𝐀𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭𝐬:• Architected and implemented end-to-end marketing models leveraging advanced machine learning techniques, resulting in a 10% annual revenue uplift through targeted recommender systems and NLP applications.• Pioneered the development of state-of-the-art generative AI solutions utilizing Retrieval-Augmented Generation (RAG) techniques, optimizing product descriptions and reducing associated costs by $100K annually.• Enhanced email marketing strategies through AI-driven content optimization, boosting engagement rates by 20% and significantly improving campaign effectiveness.• Orchestrated the integration of large language models (LLMs) with domain-specific knowledge bases, enabling more accurate and contextually relevant AI-generated outputs.𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞:• Leveraged a diverse tech stack including Python, Langchain, PySpark, Airflow, and AWS services (S3, SageMaker, Glue, ECS, EC2) to build scalable and efficient ML pipelines.• Implemented containerization strategies using Docker to ensure reproducibility and ease of deployment across different environments.• Utilized Pinecone for efficient vector similarity search, enabling rapid retrieval of relevant information in RAG applications.• Developed interactive dashboards using PowerBI and Tableau to visualize complex data patterns and communicate insights to stakeholders effectively.

• Led the deployment of the Fay-Herriot model for Small Area Estimation, significantly enhancing the precision of poverty mapping of Indonesia, pivotal in guiding socio-economic development strategies.• Orchestrated the harmonization of diverse data streams—household surveys, AIS, census, and GeoQuery data—through advanced ETL processes, ensuring high data integrity for strategic analysis.• Utilized Tableau and Python to transform complex socio-economic datasets into compelling, actionable insights, influencing key policy decisions and enabling targeted strategic interventions.

𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐕𝐨𝐢𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐂𝐲𝐜𝐥𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 (𝐑𝐂𝐌) 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲.• Spearheaded the development and deployment of a voice analytics model, reducing claims processing times and directly impacting over 100,000 claims annually.• Conducted in-depth analysis of over 60,000 voice logs using advanced analytical tools, uncovering pivotal patterns that inform optimal engagement strategies.• Implemented a machine learning model to predict the best call times to insurance companies, achieving a 67% reduction in wait times for billing specialists and generating $600K in annual cost savings.𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐂𝐨𝐝𝐢𝐧𝐠 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧.• Designed and deployed an NLP model leveraging Named Entity Recognition (NER) to automate medical coding from Electronic Health Records (EHRs), processing over 120K files annually.• This innovation led to a 55% reduction in coding time, significantly enhancing operational efficiency and contributing to $8M in annual savings by streamlining medical coding processes.𝐂𝐥𝐚𝐢𝐦 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐀𝐬𝐬𝐮𝐫𝐚𝐧𝐜𝐞 𝐓𝐨𝐨𝐥• Architected an AI-driven tool to enhance claim validation by accurately detecting billing errors in over 50K cases annually, thereby achieving an impressive 98% accuracy rate.• Our approach to prioritizing high-risk claims for auditor review not only ensured heightened claim accuracy pre-submission but also yielded a 15% operational savings and halved claim rejection rates.

Lead the design vertical of the team (MECHANIZERS 23.0) to compete in the Electric go-kart championship and achieved the lightest vehicle in the competition and finished as a 2nd runnerup.