Develop and deploy AI/ML technologies to enhance investment decisions in the real estate market

A late-stage "unicorn" SaaS CRM startup providing commercial real estate leasing and asset management technology solutions- Spearheaded end-to-end solutions for investment and asset management, marketing automation, and property management at the #1 SaaS CRM startup for Commercial Real Estate clients (1 of 3 founding data scientists) - Designed and productionized a commercial space data set generator using a Regression, Decision Tree, Random Forest and XGBoost for comparative market analysis, reducing the BI and Data teams’ redundant queries by over 23% - Automated weekly market reports for institutional investors, saving 21+ hours monthly, by developing a property recommender, dashboard, and an ML pipeline using Looker, AWS SageMaker, S3, and Snowflake - Developed a proof of concept tenant renewal prediction model to explore 3 new tenant data projects using Logistic Regression in Python with scikit-learn, SQL, AWS SageMaker notebooks, S3 integrating data into warehouse- Migrated complex data warehouse and siloed in-house data to Snowflake, reducing query times by 12 hours/month, enabling reproduction of error-free data and trend analysis faster for the market activation and BI teams

Delivered data science-driven CRE investment recommendations to internal and external stakeholders including Investment Sales and Institutional Investor clients, via the implementation of data mining, analysis, and visualization solutions - Initiated an alternative data strategy to find alpha signals by sourcing and analyzing employment data, mobile location data, air traffic data tracking hotels / commercial real estate market recovery that sped up the acquisition process, resulting in $350MM+ in office and hotel investment revenue during the COVID-19 pandemic - Established a data archiving strategy compressing CSV files and developed ETL pipelines to 5 hours faster per operation with reproducible data access, using PostgreSQL, AWS S3, and Dask- Created compelling data insights and visualizations to enhance demographic analysis and investment appetite on commercial assets, using Flask, Dask, Plotly, Folium, Mapbox, and GeoPandas for investment sales- Built the market analytics tool and dashboard to automate weekly reporting process, aggregating 30 years of siloed proprietary statements for trend analysis and saving 40+ hours of work/month using Flask, Plotly and mapbox- Developed multiple web scrapers to reduce data subscription costs by 18% using Scrapy, Splash, Selenium, and bs4 to scrape data and performed NLP tasks such as parsing, text pre-processing, and named entity recognition

Cornell Tech incubated Intensive data science fellowship for STEM Ph.D. and Masters with <2% admission rate. The curriculum includes • Machine Learning, Natural Language Processing, Data Structure and Algorithm, Distributed Computing, Data Visualization, Web Application Development• Programming Languages and Tools: Python, SQL, Spark, Hadoop, MapReduce, Git, Unix, Heroku, Flask, AWS

Analyzed how CEO’s risk-taking personality trait impacts on firm’s acquisition performance• Collected and conducted NLP analysis on unstructured data - shareholder letters, firm acquisition records, CEO biographies• Developed multi-variate regression models and machine learning algorithms for hypothesis testing

Examined the role of social networks and creativity in shaping early 20th century abstract artists’ fame• Collected and processed artists’ artwork images, biographies, letters and historical text data for machine learning applications• Quantified the fame, creativity, and social network of the artists for advanced statistical analysis

Responsible for credit risk model methodology implementation and risk analysis on the firm’s profit and loss• Developed and implemented methodologies to evaluate total company counterparty credit risk exposure• Enhanced Internal Models Methodology (IMM) by providing analysis on Risk Weighted Assets (RWA) • Conducted sensitivity analysis by asset class, region and industry to measure and manage portfolio risk• Worked with Sales and Trading, Quants and Credit Underwriting Groups to mitigate risk exposure

Applied to and selected by the most competitive team within the risk rotational program • Enhanced computational accuracy of Value-at-Risk (VaR) backtesting and the attribution of profit and loss • Analyzed market risk profile of trading businesses using VaR model, scenario analysis and statistical analysis• Produced VaR backtesting reports for C-level executives, MDs, and regulatory examinations

Hired into the highly selective 2-year risk rotational analyst program; placed in the CFO Compliance • Built streamlined compliance issue monitoring models with enhanced compliance metrics for CFO group

EDP Renewables North America LLC., Investments and M&A Analysis Division: • Implemented valuation analysis for M&A transactions in renewable energy industry• Performed pricing strategy and sensitivity analysis for Origination team to bid wind farm into Power Purchase Agreements and state auctions

Investment Banking Division: • Conducted extensive due diligence, data mining and industry research for presentations used in potential M&A deals• Assisted in localization of financials for cross-border projects by following domestic rules and regulations• Drafted pitch book and assisted with DCF analysis for potential acquisition of $5.5 billion construction company

Audit and Assurance Department: • Assisted audit staff with due diligence process for major domestic companies in consumer products industry• Ensured audits are performed in accordance with regional accounting standards and department procedures