Résumé
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Contact Information
| Name | Nahyeon Bak |
| Professional Title | PhD Economist & Data Scientist |
| nahyeonbak@gamil.com | |
| Location | Washington, DC |
| Nationality | South Korean | U.S. Permanent Resident |
Professional Summary
PhD Economist and trusted technical advisor with 7+ years’ experience shaping organizational strategy through advanced analytics and causal inference. At Amazon, owned end-to-end initiatives generating $224M+ in cost savings, translating ambiguous business challenges into scalable analytical solutions. Recognized for defining methodological standards adopted across teams, influencing VP-level decisions, and building cross-functional partnerships spanning benefits, clinical, pharmacy, and technology organizations. Track record of mentoring economists through promotion cycles and scaling scientific approaches enterprise-wide.
Experience
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2023 - Arlington, VA
Economist
Amazon, PXTCS Benefits Research Team
- Led end-to-end development of medical plan enrollment and pricing models (conditional logit, elasticity), achieving <1% error and driving $205M+ in cost savings through optimized plan design and pricing
- Built ML-based medical spending forecasts (XGBoost, LightGBM, Lasso) to evaluate cost scenarios and inform risk-adjusted pricing strategies
- Designed and implemented causal inference frameworks (A/B tests, staggered DiD) to measure impact of benefits changes on enrollment, retention, and cost outcomes
- Created Long-term Economic Value (LEV) framework linking engagement to downstream health and cost outcomes, enabling investment prioritization
- Established measurement infrastructure (30+ KPIs) for benefits platform performance, now guiding director-level strategy and campaign optimization
- Drove cross-functional initiatives (e.g., personalized benefits recommendations, vendor/GLP-1 ROI analysis), influencing VP-level decisions and enterprise benefits strategy
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2020 - 2023 Arlington, VA
Economist
Amazon, PXTCS Compensation & Benefits
- Influenced VP-level wage decisions through causal impact analyses measuring effects of hourly wage changes and internal transfer policies on attrition, hiring, and productivity (DID, Synthetic DID)
- Drove 401(k) matching policy for student loan payments – analysis demonstrated value exceeding hourly wage increases, directly influencing enterprise-wide adoption
- Led five enterprise conjoint surveys (Benefits, Compensation, Virtual Care, Health Benefits, Earth’s Best Employer) identifying key drivers of hiring and retention; established survey best practices now documented in internal Wiki
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2019 - 2020 Seattle, WA
Economist
Amazon, Device Economics Team
- Built foundational expertise in conjoint analysis and demand forecasting models for Amazon devices
- Conducted price elasticity analyses using instrumental variable regression to inform dynamic pricing strategies
- Developed consumer preference models informing product pricing and feature prioritization
Education
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2014 - 2019 Seattle, WA
PhD in Economics
University of Washington
- Dissertation Essays on Dynamic Consumers’ Brand Choice
- PhD Advisor Patrick Bajari
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2012 - 2014 Minneapolis, MN
M.S. in Applied Economics
University of Minnesota
Skills
Professional Skills: Creative Problem-Solving, Research Management, Strategic Thinking
Econometrics/Statistics: Causal Inference (DID, Synthetic Controls, Propensity Matching), Structural Estimation, Discrete Choice (Logit/Probit/Mixed Logit), Bayesian Hierarchical Models, Survival Analysis (Cox PH, Kaplan-Meier), Conjoint Analysis, Survey Design/Analysis
Machine Learning: Light GBM, Random Forest, Gradient Boosting, Lasso/Ridge Clustering (K-means/Hierarchical)
Programming: Python (Polars, Scikit-learn, pandas, NumPy), SQL, R, STATA
Data Platforms and Visualization: Snowflake, Databricks, AWS (S3, SageMaker), Tableau, Power BI, AWS Quicksight
Survey Tools: Sawtooth (CBC/ACBC), Qualtrics
Collaboration Tools: Git, Jira, Asana