{"$schema":"https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json","basics":{"name":"Eric","label":"Data Analyst","summary":"Data analyst with 4+ years of experience building predictive models, dashboards, and analytics pipelines. Delivered $11M+ in annual savings at Hyundai through time-series analysis and operational KPI optimization.","location":{"city":"Los Angeles, CA, United States"},"profiles":[{"network":"LinkedIn","username":"eric-cwkim","url":"https://linkedin.com/in/eric-cwkim"},{"network":"GitHub","username":"eric-cwkim","url":"http://github.com/eric-cwkim"}]},"meta":{"canonical":"https://saywise.com/humanreal","version":"v1.0.0","lastModified":"2026-09-08T03:18:51.146Z"},"x_saywise":{"handle":"humanreal","pronouns":null,"availability":null,"oneLiner":"Data analyst with 4+ years of experience building predictive models, dashboards, and analytics pipelines. Delivered $11M+ in annual savings at Hyundai through time-series analysis and operational KPI optimization.","profileUrl":"https://saywise.com/humanreal","markdownUrl":"https://saywise.com/humanreal/profile.md","pdfUrl":"https://saywise.com/humanreal/resume.pdf","archetype":{"code":"HE-MG","name":"Junction Trailblazer","url":"https://saywise.com/sca/e5be9d7c936f","rarity":"One of the first Junction Trailblazers on Saywise"},"aiStack":[{"name":"Claude Code","description":null,"url":"https://claude.com","storyCount":0},{"name":"Claude","description":null,"url":"https://claude.ai","storyCount":0}]},"work":[{"name":"Handshake","position":"Handshake AI Fellow","url":"https://joinhandshake.com","startDate":"2025-09-01","endDate":"2025-12-01","summary":"• Evaluated and validated LLM-generated outputs to ensure factual accuracy, logical consistency, and reduced bias, significantly improving the reliability of datasets for downstream analysis. • Analyzed AI response patterns across diverse prompt scenarios to identify core error typologies, providing data-driven, actionable feedback to enhance overall model performance. • Processed and structured complex unstructured data through rigorous annotation, transforming raw inputs into standardized formats ready for advanced analytics."},{"name":"University of Michigan - School of Information","position":"Research Data Scientist","location":"Ann Arbor, Michigan, United States","startDate":"2025-08-01","endDate":"2025-12-01","summary":"• Conducted extensive data collection and preprocessing to enhance public datasets and partner organization data for research purposes. • Designed and implemented predictive models and algorithms using advanced machine learning and AI techniques. • Validated model performance through rigorous experimentation, ensuring alignment with research objectives."},{"name":"Hyundai Motor Group","position":"Strategy & Operations Manager | Business Analytics","location":"San Jose, California, United States","url":"https://hyundaimotorgroup.com","startDate":"2020-01-01","endDate":"2024-03-01","summary":"Led data analytics initiatives across predictive maintenance, EV charging expansion, and operational optimization. Built time-series anomaly detection models that reduced unplanned downtime by 40% and saved $10M+ annually. Analyzed 10K+ EV charging transactions to calculate station-level ROI metrics informing expansion of 5+ charging hubs. Designed Power BI dashboards tracking approval workflows and process KPIs, cutting lead times by 50% and annual costs by $1.5M. Standardized performance data for 200+ startups and automated data consolidation pipelines, reducing manual work by 70%."}],"education":[{"institution":"University of Michigan","url":"https://umich.edu","studyType":"Master's Degree in Data Science","startDate":"2024-08-01","endDate":"2026-08-01"},{"institution":"Hanyang University","studyType":"Bachelor of Science in Mechanical Engineering"}],"projects":[{"name":"Checkout A/B Test Analysis | E-Commerce User Session Data","description":"Analyzed 50K+ e-commerce user sessions using Python and SciPy to evaluate checkout flow changes. Validated statistical significance and translated results into checkout optimization recommendations."},{"name":"Cohort Retention Dashboard | SaaS User Activity Data","description":"Built SQL-based cohort analysis tracking 30/60/90-day retention across monthly user cohorts. Designed Tableau dashboard with retention heatmaps and churn-risk segmentation to identify drop-off points."},{"name":"dbt Analytics Pipeline | E-Commerce Orders and Customer Data","description":"Built end-to-end analytics pipeline in dbt and PostgreSQL, transforming raw order and customer data into 12 tested staging and mart models. Developed KPI-ready aggregate tables that accelerated Tableau reporting with consistent data marts."},{"name":"Rainy vs. Clear Day Accident Analysis – NYC Collision Data","description":"Cleaned and integrated 196K+ NYC collision records with weather and geolocation data using Python Pandas. Visualized crash hotspots using Folium and identified weather-correlated risk patterns."},{"name":"Integrating Multi-Source Movie Data for Rating Prediction and Recommendation","description":"[Data Integration] - Built a preprocessing pipeline integrating 'IMDb', 'OMDb', and 'MovieLens' movie datasets with Python, including missing value handling, feature standardization, and schema consolidation to create a unified dataset for NLP-based movie rating prediction and recommendation modeling. [Modeling and Analysis] - Developed supervised learning models (Logistic Regression, Random Forest, XGBoost, KNN) with cross-validation and hyper-parameter tuning, achieving improved predictive performance through feature engineering and ensemble methods for movie rating prediction. : movie rating prediction with improved performance (best model: XGBoost, ~69% accuracy / 0.67 F1) [Result] - Delivered a unified NLP pipeline that improved movie rating prediction accuracy and generated insights for content recommendation, highlighting the value of integrating multi-source data with machine learning."},{"name":"Data-Driven Analysis of Nursing Home Funding Inequities","description":"- Produced reproducible workflows enabling MEJI to refresh analyses with future cost report updates. - Designed a data dictionary and merged financial records with census/NH demographic datasets at the ZIP-code level. - Applied statistical testing, z-score–based outlier detection, and resampling to evaluate regional disparities."},{"name":"Integrated Investment Data & Management Platform","description":"CRM Data Integration Pipeline • Built web-based dash board and Airtable data pipelines to consolidate CRM data and designed a cross-department approval workflow, reducing investment decision-making time by 50%. Post-Investment Management System • Architected and deployed a web-based monitoring platform with Power BI dashboards and Kanban boards, enhancing transparency for 20+ stakeholders and boosting efficiency by 30%. Data-Driven Decision Enablement • Standardized reporting and approval workflows, providing executives with real-time visibility into portfolio performance and risks to ensure faster, data-driven decisions."}],"skills":[{"name":"Analytical Skills"},{"name":"Python (Programming Language)"},{"name":"PostgreSQL"},{"name":"MySQL"},{"name":"Tableau"},{"name":"Jira"},{"name":"Microsoft Power BI"},{"name":"SQL"},{"name":"Git"},{"name":"R (Programming Language)"},{"name":"Seaborn"},{"name":"Pandas (Software)"},{"name":"Data Quality"},{"name":"Product Analysis"},{"name":"Statistical Data Analysis"},{"name":"Hypothesis Testing"},{"name":"Snowflake"},{"name":"Business Impact Analysis"},{"name":"Data Wrangling"},{"name":"Extract, Transform, Load (ETL)"},{"name":"Matplotlib"},{"name":"Business Strategy"},{"name":"Data Analysis"},{"name":"Machine Learning"},{"name":"NumPy"},{"name":"Feature Engineering"},{"name":"Data Integration"},{"name":"Time Series Analysis"},{"name":"Data Visualization"},{"name":"Data Pipelines"},{"name":"Information Visualization"},{"name":"BigQuery"},{"name":"dbt"},{"name":"GitHub"},{"name":"Docker"},{"name":"AWS S3"},{"name":"AWS EC2"},{"name":"Airtable"},{"name":"Excel"},{"name":"Datadog"},{"name":"SciPy"},{"name":"Cohort analysis"},{"name":"Retention analysis"},{"name":"Funnel analysis"},{"name":"Anomaly detection"},{"name":"EDA"},{"name":"Folium"}],"certificates":[{"name":"Intermediate PostgreSQL","issuer":"University of Michigan"},{"name":"Supervised Machine Learning: Regression and Classification","issuer":"DeepLearning.AI"},{"name":"Claude Code in Action","date":"2026-03-01","issuer":"Anthropic"}],"publications":[{"name":"Requirement Analysis of Technology Foresight Process Design for Communication Systems in the Automotive Industry","releaseDate":"2020-11-18","summary":"Published in The Journal of Korean Institute of Communications and Information Sciences (Nov 2020) SUMMARY: - Conducted quantitative foresight analysis to map disruptive patterns in telecommunication and vehicle network systems. - Analyzed communication & sensor data systems in the automotive domain to identify requirements for future-proof technology architectures. - Proposed a data-driven framework for evaluating innovation readiness and cross-domain technology adoption in connected-car ecosystems. - Integrated system modeling and qualitative analytics to improve flexibility and decision-making in R&D investment planning."}]}