In today’s competitive market, companies that make quick, informed decisions with large datasets enjoy the benefits of millions of dollars in savings and profits. As a Senior Principal Data Scientist at RTX, I develop and deploy analytic software products to make real-time data-driven decisions as part of the Applied AI team. With experience and certifications in cloud computing (AWS, Azure) and machine learning (TensorFlow, PyTorch, Scikit-learn), I’m a key contributor in creating large-scale software solutions with the flexibility to add requirements as new business values are recognized. This pairs well with my mechanical engineering experience to develop digital twins of electromechanical systems to predict when repairs are needed. Some of my machine learning solutions include recommendation systems to find alternative parts for obsolete ones, deep learning for predictive financial analytics, and computer vision models to detect patterns in large sets of images.My research experience has kept me current on the latest technologies within the field of data science. For my Ph.D. dissertation, I applied machine learning techniques to develop a framework for an automated stroke rehabilitation system using inertial measurement units (IMUs). At RTX, as a principal investigator and R&D team member, I researched the latest techniques in anomaly detection, natural language processing, and data wrangling. These efforts resulted in the creation of several intellectual properties.More importantly, capturing customer requirements and planning the work with the team is the key to success. Each project is treated as a start-up where the business model canvas is used to capture the problem statement and the business value of the solution. The Scaled Agile Framework is then used to plan tasks to accomplish our goals along with collaborative tools such as GitHub, Jira, and Confluence to coordinate efforts with my team members.✨✨✨ Expertise summary ✨✨✨✔Focus: mechanical and systems engineering, machine learning/artificial intelligence, control systems, dynamics, and motion capture.✔Programming experience: C++, C#, Git, Javascript/Typescript, Matlab, and Python.✔Data science/ML experience: anomaly detection, computer vision, data wrangling, deep learning, large language models, and time series forecasting.✔Hardware experience: Arduino, Raspberry Pi, IMU, optical tracking systems.