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Schedule Interview NowMy name is David Y. and I have over 13 years years of experience in the tech industry. I specialize in the following technologies: Recommendation System, Python Scikit-Learn, pandas, Python, Data Science, etc.. I hold a degree in Other. Some of the notable projects I’ve worked on include: USAID Social Media Sentiment Analysis and AI RAG MVP, Founder and Maintainer OSS Project: Ragtastic, Developed a Custom AI Chat Agent for Astrology.com Competitor, ODSC 2020 Key Speaker on Data Visualization, Net worth recurrent model, etc.. I am based in Los Angeles, United States. I've successfully completed 17 projects while developing at Softaims.
I approach every technical challenge with a mindset geared toward engineering excellence and robust solution architecture. I thrive on translating complex business requirements into elegant, efficient, and maintainable outputs. My expertise lies in diagnosing and optimizing system performance, ensuring that the deliverables are fast, reliable, and future-proof.
The core of my work involves adopting best practices and a disciplined methodology, focusing on meticulous planning and thorough verification. I believe that sustainable solution development requires discipline and a deep commitment to quality from inception to deployment. At Softaims, I leverage these skills daily to build resilient systems that stand the test of time.
I am dedicated to making a tangible difference in client success. I prioritize clear communication and transparency throughout the development lifecycle to ensure every deliverable exceeds expectations.
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Initial consult to define data requirements, meet with data vendors, and define scope and methodology for data collection and MVP for USAID sentiment and social listening concept.
Many of my clients have recently asked me to lead retrieval augmentation generation / generative search projects. Since many of my clients have everyday needs, I built an open-source framework that makes it easy for me to import their data, apply embeddings, and serve a UI for fast prototypes. This makes it easy to assess these types of projects quickly and helps keep the cost down by using my own set of frameworks and tools that I developed within a familiar web framework that can quickly be put online within a client's infrastructure.
Developed AI chat agent for Astrology.com competitor, integrating proprietary knowledge base. Defined metrics, prepared data for embeddings, built prototype frontend/backend. Used OpenAI API, ChromaDB for retrieval, React frontend, FastAPI backend. Managed entire project: scoping, data prep, implementation. Ensured data security and accuracy. Showcases AI integration, full-stack development, and handling of sensitive industry data. Delivered functional prototype meeting specific client requirements, demonstrating ability to execute complex, tailored AI solutions.
Data visualization is fundamental to the data science process. Using plots and graphs to convey a complex idea makes your data more accessible to everyone. In this session, you will learn the fundamentals of plotting with Pandas in Jupyter by building an interactive visualization prototype that can also run as a standalone web application/dashboard. This session is for anyone who wants to be more familiar with data visualization, hands-on, with Python, Pandas, Matplotlib, interactive widgets, and Flask. Learning Objectives Part 1 - Be familiar with data visualization conventions within Jupyter Lab - Implement standard plots using Pandas - Be able to describe the integration between matplotlib and Pandas - Understand the concept of "figure" and "axes" Part 2 - Be familiar with other visual frameworks and assess their strengths - Panel fundamentals - Implement a basic prototype visualization for exploring data with interactive elements Part 3 - Tradeoffs between sharing notebooks vs. custom dashboards - Flask Fundamentals - Migrate a Panel visualization in Jupyter to Flask
A boutique, stealth start-up approached me for some basic analytics work with very minor modeling goals in mind. The client had these goals in mind: 1) Overall view of sales by state, daily trends, and basic cohort statistics in a dashboard that is easily navigated. 2) Purchase behavior and trends 3) Viability to increase IAP (in-app purchases) based on impressions of products and search funnels 4) Identification of seasonal trends and search history With these goals in mind, I set up a basic dashboard using Metabase on my local system using a snapshot of the clients transactional database. Through exploratory analysis, I built a set of reports that identified the most dominant trends overall and specific user behaviors that could be attributed to specific demographics within the population of customers. Some of the identified trends: - Time of day purchases - App vs Web purchases - Spending habits based on gender and category of spend group (big vs medium vs small) - Keyword searches and conversion behavior of spend groups After the exploratory phase of the project, I modeled user behavior of specific groups to a set of features that I engineered (time of day, time since last purchase, # of visits to product details), and produced a linear based model that predicted time till purchase and a multi-variate classification model that described which features influenced the class prediction of conversion by spending group. From this approach the client was able to optimize their marketing and recommendation algorithms to show more relevant items based on users' spending history, preferences, and spin-off targeted email campaigns per user.
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