Eun-Young Christina Park

A Data analytics professional specializing in machine learning, NLP, and advanced analytics, with experience in healthcare, finance and regulatory domains. Skilled in Python, SQL, and scalable data pipelines, with a proven ability to transform complex datasets into actionable insights and support strategic business decisions. Interesting in helping people and improving social environments.

Research Scientist/ Data Scientist

R&D Central Research, Microsoft

Oct 2021 – Aug 2024

• Designed and executed ETL workflows in Python to extract radiology reports, transform unstructured text into structured datasets and load them into LLM pipelines for healthcare analytics within an Azure cloud environment
• Re-engineered and optimized scalable data pipelines in Python to process large unstructured datasets, improving efficiency by 50% and enabling downstream analytics and model development.
• Developed a human evaluation process to assess LLM output, detecting hallucinations and quality degradation not captured by ROUGE metrics and supporting strategic business decisions
• Engineered features and anonymized personal information to ensure data privacy and quality
• Partnered with product managers to align data pipelines with customer requirements, ensuring insights directly informed digital experience improvements
• Trained chatbots with Python API and conducted benchmarking analysis to evaluate the performance of various NLU systems across multiple datasets and training regimes
• Created visualizations to report LLM and NLU system performance
• Used Python, Pandas, Scikit-learn, XGBoost, LightGBM and Altair to evaluate models, develop data preprocessing pipelines and report performance
• Participated in Agile development cycles

Senior Supervisor

Market and Model Risk Divisions, OSFI

Jan 2018 – Aug 2021

• Analyzed market, liquidity, and model risk data to identify trends and provide insights to stakeholders 
• Collaborated with bank stakeholders to evaluate equity and credit risk methodologies, improving risk management practices
• Translated complex risk model findings into actionable recommendations for institutional teams 

Quantitative Analyst

Commodities and Energy, RBC Capital Markets

Nov 2012 – Dec 2017

• Analyzed pricing impacts on commodity derivatives to assess business risk and profitability, delivering insights that informed trading strategies and market entry decisions
• Performed data analysis for oil, natural gas, metal, and power derivatives, enabling the business to build a realistic and effective risk management framework 
• Gathered requirements from traders and risk managers to design and deliver data systems that supported trading strategies, improved risk management, and enabled entry into new commodity markets

Master of Data Science – Computational Linguistics

University of British Columbia, Vancouver

Sep 2020 – Jun 2021

• Relevant Courses: Supervised and Unsupervised Learning, Corpus Linguistics, Sentiment Analysis, Feature and Model Selection, Machine Translation, Parsing, Computational Morphology