Professional Experience

June 2025 - Present

• Lead AI and Data strategy and transformation across Amgen’s global Operations organization, translating enterprise priorities into roadmaps, investments, and measurable outcomes.
• Chair a cross-functional AI leadership council spanning Manufacturing, Quality, Supply Chain, Regulatory, and Process Development to prioritize initiatives and align investments.
• Establish governance frameworks and operating models for Generative and Agentic AI, enabling responsible adoption in complex environments.
• Position AI and Data as core pillars of Operational Excellence, driving improvements in product complaints, reliability, efficiency, and technology transfer.
• Advise executive and senior leadership on AI and Data strategy, emerging opportunities, investment priorities, risk, and long-term value realization.
• Lead portfolio prioritization and transformation execution across AI and Data initiatives, aligning stakeholders, resources, and performance measures.

August 2022 - June 2025

• Built and led a multidisciplinary team of 10 data scientists and engineers delivering production-grade AI solutions across global Operations.
• Spearheaded adoption of Generative and Agentic AI, delivering 30%+ efficiency gains across documentation, compliance, and operational workflows.
• Established enterprise AI capabilities, standards, and scalable deployment frameworks to enable secure and reliable adoption in complex environments.
• Led development of AI-powered decision support and automation solutions, translating complex operational challenges into scalable products.
• Partnered across Manufacturing, Quality, Supply Chain, and Regulatory to identify, prioritize, and deliver high-value AI opportunities.

February 2020 - August 2022

• AWS Administration & Best Practices: Assumed the role of AWS admin for the DIPT organization, implementing best practices to ensure compliance with IT policies and enhancing the security and efficiency of cloud operations.
• Strategic Planning & Alignment: Developed roadmaps, defined measurable goals, and established key performance metrics to align analytics initiatives with Amgen’s operational objectives, fostering collaboration and inclusivity across teams.
• AI & NLP Applications: Managed a team of three data scientists to design and implement innovative forecasting and natural language processing (NLP) solutions using AWS Textract and Comprehend, driving efficiency for the Process Development business unit.
• NLP Tool Development: Led the creation of novel semantic search tools using Seq2Seq models, Transformers, and Classification algorithms, transitioning the organization from data-rich to decision-smart.
• Collaborative Development: Partnered with cross-functional teams to deploy robust NLP applications, including Q&A systems, semantic search engines, and classification models, utilizing techniques like TFIDF, LSTM, and Transformers.
• Demand Forecasting Impact: Developed and deployed advanced forecasting models (LSTM, ARIMA, PROPHET) to improve prediction accuracy and reduce inventory costs, contributing to operational efficiency and cost savings.
• Scaling NLP with AWS Sagemaker: Established best practices for scaling NLP models within Amgen using AWS Sagemaker, improving data discoverability and ensuring data integrity across millions of healthcare documents.
• Team Coaching & Project Management: Coached team members on effective project management and communication strategies, ensuring stakeholder requirements were met within tight deadlines while maintaining high-quality deliverables.

June 2019 - February 2020

• Team Leadership: Managed a team of two data scientists, driving the development of forecasting and NLP applications to address critical challenges for the Process Development business unit.
• Competitor Intelligence Platform: Designed and implemented a business intelligence monitoring system to analyze health-related content in competitor intellectual property filings, providing actionable insights for internal strategy formulation.
• Automated Metrics Reporting: Built and deployed a platform that automated the tracking and reporting of technical and business metrics for senior leadership, enhancing decision-making with real-time insights.
• Innovative Anomaly Detection: Engineered a patent-pending multivariate statistical method for anomaly detection, achieving 94% accuracy, significantly improving the reliability of predictive systems.
• Model Validation Excellence: Led the development of Standard Operating Procedures (SOPs) for model validation, ensuring consistency, compliance, and robustness in machine learning applications.
• Collaboration for Production Deployment: Partnered with software engineers to validate and transition machine learning models from research to production, ensuring scalability and operational efficiency.

November 2017 - June 2019

• Machine Learning for API Development: Leveraged machine learning techniques to identify optimal API method development conditions, addressing unmet scientific and operational needs.
• Advanced Predictive Modeling: Built a timeseries forecasting model for instrument LCAP with 88% accuracy, reducing development timelines and enhancing resource planning.
• Data-Driven Efficiency Analysis: Created SQL-based reports and machine learning models for instrument utilization and efficiency analysis, enabling data-informed decision-making.
• Analytical Chemistry Expertise: Conducted API and impurity quantification using advanced techniques such as LCMS, NMR, and GC, contributing to the precision of product development.
• Innovative Lab Practices: Introduced advanced electronic lab notebook strategies, improving the documentation and reproducibility of experimental workflows.

Laboratory Scientist, Minbiole Lab                                                                             

Aug 2015 - Aug 2017

• Machine Learning for Chemical Prediction: Developed machine learning models and molecular dynamics (MD) simulations using Python, Keras, and RDKit to predict chemical properties, advancing data-driven insights for compound potency and efficacy.
• Dataset Creation for QACs: Designed, synthesized, and analyzed hundreds of Quaternary Ammonium Compounds (QACs), building a comprehensive dataset to train machine learning models for classification and predictive analytics.
• Simulation-Driven Potency Prediction: Developed MD simulation models to evaluate and predict QAC potency, enhancing the understanding of structure-activity relationships.
• Scikit-Learn Applications: Applied scikit-learn machine learning techniques for compound classification, leveraging Python to drive computational chemical analysis.
• Scalable Compound Production: Scaled up the synthetic production of QACs to kilogram quantities, supporting downstream applications and testing at a larger scale.
• Advanced Analytical Techniques: Purified and characterized compounds using LCMS and NMR, ensuring high-quality results and reliable experimental outcomes.
• Laboratory Data Management: Designed and implemented a laboratory database system, streamlining data organization, retrieval, and analysis to improve workflow efficiency and collaboration.

Research Collaborator, High Performance Computing                                                                          

Aug 2015 - Aug 2017

• HPC-Enabled Machine Learning: Leveraged high-performance computing (HPC) clusters, including BlueWaters, to develop machine learning models for chemical potency prediction, accelerating computational workflows and enabling large-scale data analysis.
• Molecular Simulations: Conducted NAMD simulations to explore the structural properties of pyridine-based ammonium salts, providing valuable insights into molecular behavior and interactions.
• Data Trend Analysis: Applied machine learning algorithms, including KMeans, KNN, and Linear Regression, to identify trends and patterns in complex chemical datasets, enabling data-driven discoveries.
• Automation with Python: Created Python scripts to automate HPC workflows, optimizing resource utilization and reducing manual intervention in computational processes.