Title: Operational and Analytical development of a volume based restaurant• Automation of high impact managerial duties including: data collection, payroll, and scheduling removing 25 hours of weekly tasks• Implementation of KPI's targeted at identifying performances for individual employees and restaurant health• Modeling analysis to find contributing factors that impacted revenue• Boosted beverage percentage of sales from 3% -> 13% of total revenue• 4.5 Star on Yelp + Google Reviews• Increased operational efficiency, reducing employee count, and server pay by 25%• Managed 40+ employees, and developed culture• Voluntary annual turnover rate of <5%

• Partnered with value-based insurance and healthcare providers to develop and improve explainable patient-centric models curtailed to preventative medical interventions.• Created in-house quality assurance tests and tools for quality-of-life improvements of the entire data pipeline including: assessing ailment frequency, commonly-used feature implementation tests, bias and fairness tests, etc.

• Pushed for and developed a new employee onboarding guide and automations that decreased onboarding time from a month to two weeks through asynchronous curated tasks.• Created interactive Tableau dashboard for C-Suite level oversight of the entire organization’s workforce and contractors, providing extensive analysis of time and resource allocation.

Lead search engine optimization implementations through redesigned website, modification of the metadata, keyword identification, and creation of real estate education content.

• LEADERSHIP: - Ensured tasks and timeline goals are met through weekly meetings.- Hosted training and peer review sessions for four interns along with providing online learning resources to nurture data scraping skills and software development skills.• BUSINESS: - Data driven decision processing and extensive analysis to verify correct course of development based on market validation, competitor analysis, and industry reporting.- Managed quick development cycles with the lean approach• DEVELOPMENT: - Used MongoDB to develop a database optimizing memory usage through normalization (NF3), combining the benefits of features such as the agility of NoSQL and organization of SQL.- Used Docker to containerize the application, allowing for future scalability, and ease of deployment on Cloud services.- Built tens of crawlers bypassing anti-crawler protections set up by other companies to aggregate information.• DESIGN:- Quick prototyping with Figma and testing, designing and showcasing the minimum-viable-product based on the expected skill set and time constraint of developers, and the highest priority issues that consumers faced in one week period.- Conducting several user experience surveys and workshops to understand consumer wants and demands.

• Analyzed popular COVID-19 detection in lung X-ray computer vision models using model-agnostic analysis techniques, discovering major flaws and potential model improvements by understanding contributing visual factors.

• Conducted weekly cycles of user-experience oriented design sprint to solve diabetes educational deficiencies in the existing market. • Utilized understanding of medical professionals and existing diabetes patients to create a graphic interface that promotes confidence of newly diagnosed patient's ability to explore management techniques.• Developed with D3.js

• Utilized Django and SQLAlchemy to develop back-end• Utilized React and Redux to develop front-end to the specification of the designers

• Developed full-stack dashboard interactive, scraping and cleaning data from third party websites and primary data sources, and used D3.js to build the service.• Simulated foot traffic of the Boston Metropolitan based on the date, time, and weather allowing restaurant owners to better understand marketing potential of passing "eyeballs."• Recommended changes in the data collection methodology through my experience in the restaurant industry to have a less biased source, and new exploratory metrics.

• Built data pipeline connecting data sources from events hosted by TESC to conduct analysis of the potential gender and racial biases of invite-only events.• Scripted an algorithm to reallocate funds appropriately based on student participation of organizations funded by TESC in the 2018-2019 school year

• Conducted statistical testing on human vs. machine interpretation on facial features such as attractiveness and aggressiveness studying the psychological aspects of human facial features comparable to the "logical" understanding of human facial features.• Experiment design and creation through Amazon’s Mechanical Turk• Quality control through statistical inference of individuals that gave faulty data, and removing them from the database