- Innovate and customize deep learning multimodal models, with a focus on Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), to address specific challenges in entity resolution, demonstrating a keen ability to experiment, fine-tune, and modify models to achieve optimal performance for our unique use case- Actively collaborate with cross-functional engineering teams, participating in regular code review sessions to ensure code quality, adherence to best practices, and knowledge transfer, fostering a collaborative environment that enhances overall team efficiency- Provide mentorship and knowledge-sharing within the team, fostering a collaborative environment and empowering colleagues with the skills and insights needed to excel in the rapidly evolving field of deep learning and entity resolution- Collaborated cross-functionally with diverse teams and leadership to lead comprehensive assessments of existing systems, identifying and prioritizing areas of technical debt, resulting in a strategic roadmap for long-term improvement and reduced maintenance costs

- Developed a global agricultural field boundary prediction product based on contemporary computer vision research, using satellite imagery, semantic segmentation via deep learning with TensorFlow and Python, and common computer vision morphological operations for post processing- As first employee, designed and deployed a regression machine learning model for predicting crop yields across millions of fields throughout 19 states in the U.S. which led to the company’s first paid clients, including one of the largest U.S. agriculture lenders- Deployed a random forest classifier to predict type of land cover at the pixel level from satellite imagery across entirety of 5 U.S. states using recent academic publications as a guide for feature selection- Experimented with Generative Adversarial Networks for generating optical images from radar images while using Variational Autoencoders to generate synthetic data to extend our existing datasets- Partnered with research scientist to create machine learning models and tailored prediction data, utilizing findings for client proof-of-concepts, ongoing business development and full-scale implementation

- Managed business partner analytics requests, analyzed technical solutions for efficacy, presented recommendations interdepartmentally across company and coordinated implementations - Queried big data using Hue, Big Query, Teradata, Oracle and MySQL; created Tableau dashboards to automate reports and uncover actionable insights- Oversaw audit of 600+ million customer records spread across 9 systems as part of a migration from a legacy system to the cloud; coordinated efforts between 4 teams - Conducted an analysis of 1000+ stores to identify optimal store hours for maximum profitability which led to an ongoing test of modified store hours nationwide

- Performed statistical analyses of customer behavior, merchant performance and A/B test results, using SQL, Python, R and Microsoft Excel- Acted as interdepartmental contact for analytics needs and problem identification, including data instrumentation requirements and prioritization of data quality issues- Presented analyses and insights to all levels of the company

- Ensured data accuracy for 2,000+ devices across 80M monthly active listeners; used SQL and Python to transform data into usable reports that drove decision making- Presented key findings and performance analytics internally and at partner meetings with Google, Apple and Microsoft - Created time series and linear regression models to set KPIs and forecast listener usage- Built and maintained personal tables as data sources to feed recurring reports, dashboards, models and deep-dive analyses - Created user-friendly Tableau dashboards; analyzed trends and reported findings to key stakeholders

- Worked cross-functionally with all departments collecting, cleaning and analyzing data to support strategies across mobile, CE, automotive, content and other verticals- Generated monthly and quarterly progress reports on key performance indicators for distribution to teams throughout company, including executive leadership and the Board of Directors- Created models to project the value of potential partnerships and presented recommendations - Conducted research on industry trends and synthesized analysis into PowerPoint presentations

- Tracked data on more than 1M customer messages per year using Hadoop/Hive- Built and customized queries, scripts and workbooks using Python, Excel, Hive and Tableau; trained staff- Created bi-weekly report on customer feedback using Python, SQL and Tableau for C-level management- Created and led Listener Support Reporting Team

- Gave personal instruction on Apple hardware and software with an emphasis on Logic audio and other audio applications- Took on reporting of team's productivity metrics by combining valuable measures from several different reports- Created a new metric to help find solutions to problem of last minute appointment cancellations

- Helped customers find the products that were the best fit for them- Worked face-to-face with a variety of customers and personalities