◦ Architected a cloud-based audio integration for commercial video recordings, embedding real-time and playback audio via WebRTC and optimizing clip storage with Azure Blob Storage, supporting millions of monthly users and reducing playback latency through frontend and network-level enhancements.◦ Enabled the reliable processing of thousands of daily SMS messages by developing a distributed messaging system with Kafka, gRPC, Twilio API, and .NET SMTP, achieving high scalability to support growing dealer demands and ensuring seamless communication.◦ Modernized Alarm.com’s On-Site Wrap-Up Feature by migrating from legacy ASP.NET Server Pages to an Ember.js front end and JSON API backend, achieving 4x faster load times and increasing dealer adoption by 40%.◦ Delivered accurate, real-time emergency dispatch information to more than 10,000 previously unsupported customers by developing two REST API integrations for alarm monitoring stations, ensuring reliable and timely communication during critical incidents.◦ Enhanced integration test coverage by automating setup, cutting build startup time by 30% and expanding coverage to critical code areas. Boosted adoption through strong collaboration with QA, developers, and PMs.◦ Increased team documentation output by 20% by designing a Python-based microservice powered by ChatGPT that automated pull request documentation, streamlining workflows and improving efficiency.◦ Mentored two interns through critical projects, developing their technical and leadership skills, and ensuring their return as high-performing full-time employees.

◦ Integrated 15+ disparate utility datasets into a 100GB PostgreSQL database by building a robust Python pipeline leveraging Pandas, SQLAlchemy, Psycopg2, and PyPDF2, streamlining data consolidation and enabling efficient access for analysis.◦ Achieved over 90% forecasting accuracy for wholesale electricity prices by developing time-series components of a Python machine-learning model using utility meter data, leveraging NumPy, Scikit-Learn, and Pandas to provide actionable insights for energy market analysis.◦ Contributed to securing a DOE research grant by researching 50+ academic and industry sources on energy markets and ‘Transactive Energy,’ laying the groundwork for developing and testing machine-learning hypotheses that enhanced modeling accuracy for predicting wholesale electricity prices.◦ Led a Machine Learning project for NRECA summer interns by formulating a dataset of 100,000+ data points from internal and public sources, designing a Random Forest Regression model that predicted individual contributions to NRECA’s political action committee, and providing actionable insights for senior leadership.

◦ Automated 80% of the company’s data entry process by engineering a Python and Excel pipeline, reducing manual effort by 16 hours per week and decreasing data-entry errors, enabling the team to focus on higher-value tasks.◦ Enabled seamless company-wide data entry automation pipeline adoption by authoring comprehensive documentation, reducing onboarding time for non-technical staff, and ensuring error-free operation for over five months after my departure.

• Collaborated with camp counselors and supervisors to create a safe and fun environment for over 100 campers, aged 7-14.• Organized and facilitated daily field trips to museums and attractions in the DC Metropolitan area.