Data Scientist/ Statistician

(Based in the Greater Seattle area, Washington)

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Hello! I enjoy applying predictive and causal statistical models to economic, political, and institutional data. I write on topics including data science, causal inference, data cleaning and visualization, data-driven social research, and election analytics, with a particular focus on housing affordability, homelessness, gender disparity in politics, elections, and the criminal justice system. If interested, click here to visit my Medium profile

Programming: Python, R, PySpark

Machine Learning: Classification (KNN, SVM), Regression Modeling (linear, logistic, regularized, time series),
Survival Analysis (Cox, Kaplan‑Meier), Random Forest, PCA (standard, sparse), clustering (K‑means, Hierarchical),
Topic Modeling (BERTopic, LDA, NMF), Transformer‑based NLP (BERT, Sentence Transformers)

Other: Causal Inference Design, Survey Research, SQL, NoSQL, PowerBI, Tableau, ArcGIS, Git, Markdown, D3, Azure, GCP, AWS

Work Experience

Economic Analyst (Research and Data Team) @ Washington State Employment Security Department

(November 2024- Present)

• Designed an interactive R Shiny forecasting tool leveraging cross‑validated Vector Autoregressive (VAR) models to simulate staffing needs under various scenarios by partnering with operations and human resources teams, driving data‑informed decision‑making and aligning with business KPIs.
• Contributed to the Azure Synapse migration by documenting data architecture, storage practices, and governance protocols, supporting scalable and compliant cloud data operations.
• Maintained data dictionary, defined and implemented data stewardship processes, including metadata management, data access standards, and retention policies to improve data privacy and data governance across departments.
• Automated request workflows using Power Automate and Azure DevOps, reducing manual workload, increasing transparency, and accelerating turnaround times through real‑time status tracking and notifications.
• Developed and tracked KPIs by partnering with product and engineering teams to evaluate the impact of a critical program change using a difference‑in‑differences approach; streamlined SQL data pipelines and built an interactive Power BI dashboard to deliver real‑time insights to business and program leadership.
• Responded to ad hoc data requests from internal and external stakeholders and produced public‑facing reports and infographics visualizing the Washington Paid Family and Medical Leave (PFML) program utilization trends, customer lifecycle and demographic shifts over time, helping external stakeholders understand the evolving impact of agency programs.

Senior Data Scientist (Election Research and Tabulations Team) @ The Associated Press

(November 2023 - November 2024)

• Developed time‑series regression models to forecast vote counts by geographic region and race; deployed models in production, enhancing pre‑election resource planning and accuracy.
• Applied K‑means and hierarchical clustering to group counties by turnout trends and socioeconomic‑demographic characteristics, supporting targeted analysis and outreach strategies.
• Improved turnout prediction accuracy to 98% using extrapolation techniques and post‑stratification weighting to account for sampling biases in early‑reporting precincts, delivering timely insights before polls closed.
• Built and deployed R Shiny applications using data from MongoDB, hosted on AWS EC2, to present real‑time election data to internal and public stakeholders with clear, interactive dashboards.
• Led weekly data science workshops, training a team of 8 researchers on data visualization, statistical analysis, and machine learning tools, fostering technical skill development and project alignment.

Data Science Intern @ Akalan Law Firm

(July 2022 - October 2022)

• Leveraged Latent Dirichlet Allocation (LDA) algorithm to detect emerging themes across 50K+ immigration documents and demographic records, enabling pattern recognition that supported strategic program evaluations.
• Applied machine learning techniques and predictive modeling tools such as scikit-learn and XGBoost to develop models that supported attorneys in making data-driven decisions and optimizing resource allocation.
• Defined and tracked key performance indicators (KPIs) to evaluate the effectiveness of paid media campaigns, enabling strategic adjustments and measurable ROI.
• Designed and built interactive dashboards using Tableau, enhancing data storytelling and facilitating clear, actionable communication with diverse stakeholders.

Teaching Assistant @ University of California Santa Barbara

(April 2018 - June 2023)

• Teaching Assistant for Statistical Methods and Political Science courses
• Selected Courses: Understanding Data, Probability and Statistics,
  US Minority Politics, Politics of the Environment
• Won the Adams‑Lee Distinguished Teaching Assistant Scholarship (x2),
  Nominated for the UCSB Academic Senate Award for Teaching Assistants

Education

• University of California Santa Barbara
  • Ph.D., Political Science
  • M.A., Statistics
• Sabanci University
  • M.A., Political Science
• Koc University
  • B.A., International Relations
  • B.A., Philosophy