Professional Experience

Amgen, Cambridge MA

Senior Manager of Data Science — OTDS Advanced Analytics

August 2022 - Present

Strategic Leadership in Generative AI Adoption: Spearheaded the operations strategy for the adoption and integration of generative AI, aligning cutting-edge technologies with organizational goals to drive innovation, enhance efficiency, and establish a competitive edge in AI-enabled solutions.
Driving Generative AI Innovation: Led a team of 10 data scientists and software engineers to develop and deploy scalable generative AI solutions, leveraging cutting-edge technologies like Python, Redis, AWS, Bedrock, OpenAI, LangChain, and Haystack. Delivered significant cost and time efficiencies by accelerating processes such as retrieval-augmented generation (RAG) and automated document creation.
AI-Enabled Business Applications: Owned and managed AI-Enabled RAG applications, enhancing information retrieval and enabling faster, more strategic decision-making.
Strategic NLP Development: Designed and implemented advanced deep learning and NLP models, transforming unstructured data into actionable insights that drive business strategy.
AI-Agent Framework Leadership: Designed and implemented advanced AI-Agent frameworks leveraging OpenAI’s GPT-4.0 and GPT-o1, enhancing traditional prompting techniques to deliver more context-aware and reliable outputs, significantly improving the accuracy and efficiency of existing models, enabling more precise decision-making and streamlined operational processes.
DevOps Modernization: Spearheaded the design of a DevOps framework on AWS, introducing CI/CD pipelines and Infrastructure as Code (IaC) to enhance deployment reliability and efficiency across development teams.
LLM Strategy Pioneer: Conducted early investigations into Large Language Model (LLM) applications, identifying high-impact opportunities to align AI-agent frameworks with organizational goals and operational strategies.
Documentation Automation: Developed AI-driven frameworks using OpenAI, HuggingFace, and LangChain, reducing documentation creation time by 30%.
Prompt Engineering Excellence: Innovated custom prompt-engineering techniques, achieving a 90% improvement in search and information retrieval reliability.
Regulatory Impact: Contributed to deep learning models with TensorFlow, improving regulatory model accuracy by up to 86%, ensuring compliance and operational efficiency.

Data Scientist — Operations Advanced Analytics

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.

Sr. Associate Data Scientist — Digital Integration and Predictive Technologies

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.

Associate Data Scientist — Process Development

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.

NovaLyse Biosolutions, Villanova PA

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.

Temple University, Philadelphia PA

Research Collaborator, High Performance Computing