Lena Arai, Ph.D.

Engineered an artificial neural network using Keras, Scikit-learn, SQL, Python, and Pandas to predict 3-month forward returns of the S&P 500 with over 90% accuracy, leveraging data from Bloomberg and FRED APIs.
Developed a proprietary investment model using a Support Vector Machine (SVM) classifier to forecast next month forward S&P 500 returns using SQL, Python, Pandas, and Scikit-learn, achieving an exceptional 4.7% alpha, through advanced quantitative analysis, strategic kernel selection, and parameter optimization.
Collaborated with the Director of Product and the Client Experience team in researching and developing innovative investment models, conducting extensive backtests, and optimizing asset allocation techniques, underscoring a blend of strategic partnership and advanced quantitative analysis.
Spearheaded content development, distilling intricate investment topics into digestible formats with charts, narratives, and graphics. Consistently delivered weekly presentations, encompassing Excel graphs, and talking points, empowering hundreds of financial advisors.

Developed optimized hedonic regression models in EViews to predict television series viewership with 85% accuracy for a 15-season broadcast series, strengthening economic litigation analyses.
Automated data cleaning, analysis and visualizations using Python, Pandas, Scikit-learn, Matplotlib, Seaborn and Excel, reducing project analysis time by 50%; carried out advanced data analytics, created complex models, and designed compelling visualizations for cross-functional teams.
Calculated over 100,000 instances of labor law violations and interest totaling over $3 million, contributing critical evidence to lawsuits.
Programmed advanced Excel functions: XLOOKUP, VLOOKUP, nested functions, and time and date functions to clean and analyze over 500,000 data points to identify California labor law violations.
Analyzed over 500,000+ employee payroll and timekeeping records using Python, Pandas, Datetime, and pivot tables to calculate labor violations for class action lawsuits totaling 500+ class members.
Leveraged Python to extract over 100,000 data points from PDFs.

Leveraged SQL, DBT, PostgreSQL, Python, Pandas, Scikit-Learn, Statsmodels, SciPy, NumPy, ResearchPy, Psycopg2, Matplotlib, Seaborn, Excel, and hypothesis testing to predict trust, applying algorithms to quantify physiological synchrony.
Estimated linear, logistic, ARCH/GARCH, and AR/ARMA/ARIMA time series regression models of physiological data collected at 1,000 samples per second in consumer behavior experiments using Python, Pandas, Scikit-Learn, Statsmodels, and Excel to predict individuals’ engagement, attention, emotion and retention of advertisements.
Managed research projects and completed quantitative analysis of consumer neuroscience experiments for clients such as Intel, American Express, SnapChat, Comedy Central, TLC, Traveler’s Insurance, The Humane Society, and Covered California.
Equipped major brands and non-profits with insights on marketing, advertisements, and business strategy, leading to the development of new products and services.
Measured audience response to advertisements on monthly consumer insights research projects, completing experiment design, data collection, data analysis, hypothesis testing, preparation of findings, and visualizations using Python, R, and Excel.

Designed hundreds of Tableau graphs using master data set for research, presentations, and textbook, "Global Power Transition and the Future of the European Union."
Constructed economic data set by combining thousands of data points from FRED, OECD, IMF, and Maddison Historical data.
Calculated forecasts and projections of inflation adjusted GDP and GDP per capita for G20 and EU countries for research, presentations, and textbook.

Estimated advanced econometric and regression models, statistical sampling, and survey analyses on thousands of data points using Excel and Gretl to inform economic expert witness testimony and calculate economic losses.
Conducted statistical analysis with Excel and research daily on client litigation ranging from labor-related issues to personal injury such as wrongful death, medical malpractice, asbestos exposure, and business litigation.

Education

08.2013 - 12.2022

08.2013 - 05.2015

08.2008 - 05.2011

Claremont Graduate University — Claremont, CA

Concentrations: Data Science; Behavioral & Experimental Economics
Interfield: Information Systems & Technology
Relevant Coursework: Causal Modeling, Big Data, & Machine Learning; Knowledge Discovery & Data Mining; Databases; CS Insights via Python Programming; Quantitative Research Methods; Time Series Econometrics; Behavioral Economics; Experimental Economics; Behavioral Neuroscience of Decision Making
Honors & Awards: Claremont Graduate University Fellowship, Department of Economics (2013 - 2017); Claremont Graduate University Travel and Material Award (December 2020)
Dissertation: Physiological Synchrony Predicts Cooperative Behavior with High Stakes

Claremont Graduate University — Claremont, CA

California State University, Northridge — Northridge, CA

College of Business and Economics

Relevant Business Coursework: Econometrics, The Use and Interpretation of Economic Data, Money & Banking, Price Theory, Law and Economics, Financial Management, Risk Management, Marketing, Operations Management, Management & Organizational Behavior, Business Communications
Honors & Awards: Certificate of Excellence in Economics
Business Honors Student

SQL; Python; AWS; Amazon Redshift; Snowflake; Tableau; Power BI; MS Azure ML; dbt; PostgreSQL; NoSQL; Microsoft SQL Server; Excel; R; GitHub; Visual Studio Code; JupyterLab; Coginiti Pro; EViews; ChatGPT
Pandas; Scikit-Learn; Statsmodels; SciPy; NumPy; ResearchPy; Psycopg2; Datetime; Matplotlib; Seaborn; Plotly
Statistical Modeling; Machine Learning; Predictive Modeling; Causal Inference; Data Mining; Hypothesis Testing; Experiment Design; Econometrics; Data Visualization; Financial Analysis; Research; Technical Writing; Presenting

Manuscript in Progress: Dynamic Time Warping of Physiologic Synchrony Predicts High Stakes Interpersonal Trust
Peer Review: Academic peer reviewer for the CODATA Data Science Journal

In Progress: Business Intelligence & Data Analyst (BIDA) – Corporate Finance Institute
In Progress: Financial Model & Valuation Analyst (FMVA) – Corporate Finance Institute
In Progress: Capital Markers & Securities Analyst (CMSA) – Corporate Finance Institute

Citizenship: U.S. Citizen.
Additional languages: Spanish
Experienced investor earning double-digit returns in personal investment portfolios, beating the S&P 500.
Multicultural background: Raised overseas in Ecuador, the Dominican Republic, and Barbados.