Designed a comprehensive Tableau dashboard to effectively monitor and manage a multi-million dollar yearly labor budget, ensuring adherence to financial and budgetary targets.Created numerous Alteryx workflows, automating data pipelines and significantly streamlining data analysis processes. The pipelines resulted in time savings of hundreds of hours per month.Collaborated on data analysis efforts for the Prime Program, overseeing hundreds of thousands of weekly labor hours and providing insights to drive operational efficiencies.Collaborated with cross-functional teams including Retail Ops, Workforce Planners, Finance, and Team Member Services to address scheduling and compliance challenges.Pioneered the establishment of standardized documentation, promoting clearer procedures, and enhanced cross-training, ultimately boosting operational efficiency and team performance.

Developed a Python-based API retrieval script to extract data from USDA websiteCreated an automated assignment allocation system using Alteryx, streamlining task distribution.Proficiently transformed vast volumes of raw data for cross-platform utilization, guaranteeing meticulous accuracy and data integrity.Created ad hoc reports for leadership using excel formulas (v-lookup, match) to clean, organize, and structure data.

Effectively transformed, cleaned, and organized large datasets ensuring accuracy to be used across all regions.

Data Science Immersive program with 480 hours of deep training on many aspects of data, focusing on the ability to analyze and convey data-driven facts from massive data sets, and predict what happens next through predictive modeling and pattern recognition. - Finding Important Commodities During COVID-19 Pandemic - Collaborated on a team to build a machine learning model using Twitter to find important commodities for COVID-19 pandemic.Gathered tweets off using a Twitter API, organized and cleaned the data using Tweepy. Performed visualizations, analysis, and used clustering models to discover trends. Once the model was able to recognize important tweets, the location was pinpointed onto Google Maps. Libraries utilized: Pandas, NumPy, Tweepy, Beautiful Soup, Regex- Understanding Disney and ‘The Great American Songbook’ Lyrics - NLP (Natural Language Processing) project to explore the contrasts between the lyrics from Disney and ‘The Great American Songbook’.Web-scraped data off multiple websites using Beautiful Soup, parsed data using NLTK Stopword, ran classification models (random forest, SVC) to discover characteristics between lyrics.Libraries used: SciKit-Learn, Requests, Matplotlib, Seaborn- Discovering Distinctions Between Sister Subreddits - Worked on a machine learning model to differentiate between two similar subreddits “CasualConversation” and “SeriousConversation”.Pulled 30,000 subreddit posts by using a Pushshift Reddit API, did feature engineering on data to use in a countvectorizer, and used a logistic regression model to discover diversity between posts.

-Executed a social media campaign, which grew the band’s social media presence over 300%.-Published weekly newsletter reaching over 5,000 subscribers with an average 12% click-through rate.-Curated an album pre-release crowdfunding campaign on Indiegogo that earned over $2,000.-Constructed and implemented a budget that helped decrease the band debt by 80% in one year.-Aided in two tours to Japan with a marketing operation, merchandise sales, and expanding the email list.-Filmed and edited 2 music videos and multiple ads that led to thousands of views on all media platforms.