Alex Buskirk

About Alex Buskirk

I am a research and data science professional with experience delivering actionable insights to drive business decisions in healthcare, tech, retail, and entertainment industries.Skills & Proficiencies:SQL, Python, R, Tableau, A/B testing, predictive modeling, econometrics, NLP, SPSS, DisplayR, Dataiku, lavaan, scikitlearn, tidymodels, xgboost, lightgbm, catboost, shapviz, sampling, docker, latex, wsl

Experience

1. Senior Voc Data Analyst

   REI

   Dec 2021 — Present · Seattle, WA, US

   Standardized enterprise-wide customer satisfaction (CSAT) goal-setting by building a predictive modeling framework (GBM + SHAP explanations / SARIMA forecasting). This replaced subjective, adhoc goal setting with seasonality-adjusted, peer-benchmarked targets (via clustering), ensuring fairness and data-driven accountability for retail, digital, and customer support teams.Advanced Customer Lifetime Value (CLV) strategy by modeling the cost of poor experiences using CSAT and RFM data. Developed a predictive workflow using PLS regression to estimate sentiment across unsurveyed interactions and designed a frequency weighting scheme to mitigate geographic and profile bias.Directed all aspects of Voice of Customer (VOC) research lifecycle from survey design to weighting and reporting. Modernized team technical governance by automating data pipelines using Dataiku, Snowflake, DisplayR, and Sigma. Established VOC GitLab environment for version control and model reproducibility. This drastically improved data quality and stakeholder self-service capabilities.Designed a \'Human-in-the-Loop\' framework to audit Qualtrics XM Discover (NLP) sentiment. Deployed an LLM agent with confidence-scoring rubrics to automate high-certainty validation, enabling the analyst team to focus strictly on \'Ground Truth\' definitions for complex, mixed-sentiment use cases.Developed a survey design methodology centered on planned-missingness (MCAR) and full information maximum likelihood (FIML). This approach helped the team optimize survey question sets by identifying latent constructs and the strongest predictors of satisfaction, while reducing respondent burden.Led an applied training program with VOC analyst team focused on SQL & R. Directed a capstone project that integrated customer feedback with operational data, introduced the team to Ridge/LASSO regularization, dominance analysis, and latent factor identification to drive more nuanced business insights.

Education

• University of Westminster

  Marketing/Marketing Management, General

  2013 — 2013

• Pacific Lutheran University - School of Business

  Bachelor of Business Administration (BBA), Marketing

  2011 — 2013

Skills

• R
• Sql
• Market Research
• Survey Design
• Data Analysis
• Questionnaire Design
• Quantitative Research