Responsibilities include:

• Developing and deploying cloud-based tools and products to empower cross-organizational collaboration and reduce process friction for data scientists.
• Building predictive services, chatbots, and automations for departments such as Supply Chain, Legal, and Human Resources, connecting various data sources to generate actionable business insights.
• Facilitating the adoption of advanced machine learning techniques, including Large Language Models (LLMs), to enhance predictive analytics.

• Led data science innovation showcases to drive actionable insights for Infor’s customers.
• Developed, experimented with, and evaluated machine learning algorithms to determine optimal approaches for diverse projects.
• Deployed and maintained end-to-end machine learning products on cloud platforms (AWS) using MLOps practices.

• Built and deployed machine learning applications on cloud platforms to provide actionable business insights.
• Conducted in-depth statistical analysis and demand forecasting to increase sales and enhance customer satisfaction.

• Aggregated 97 million medical concepts from databases like UMLS to create a targeted dataset for link relationship prediction in the biomedical domain.
• Designed probabilistic feature extraction methods for entity relationship data, improving prediction accuracy by 30%.
• Developed a state-of-the-art multilevel SVM library for large and imbalanced datasets in collaboration with BMW Group, achieving 100x faster runtime than LIBSVM.
• Built an ML pipeline for text categorization using Python, C++, NLTK, SVD, and SVM for BMW Group.
• Improved classification quality by 15% and 33% on two minority classes of BMW Group customer surveys.

• Redesigned the service flow of wireless access gateways, firewalls, and load balancers to enhance network scalability and performance.
• Improved Internet service quality for over 20 million users, reducing service downtimes by 40% through innovative rerouting strategies.

• Enhanced service quality by planning and executing service migrations and network upgrades.
• Reduced network latency and improved customer satisfaction by optimizing network flow and performance.