Professional experience

Develop 5 key accuracy metrics, which evaluate relative performance of 20 mainstream oil and gas price forecasts and forward curves compared to actual prices for 23 proprietary hedge funds. Build Tableau dashboard to visualize price forecasts and forecast accuracy metrics for 20 oil and gas market data, saving 5 hours per week of manual reporting work. Develop ETL solutions for market- and asset-level gas and oil data, which reduce average processing time from 7 days to under 5 minutes, utilizing Databricks, Pyspark, Python, SQL, AWS, and Airflow. Train and mentor team members on new technologies such as Databricks, PySpark, and AWS S3 to set up and manage end-to-end data pipelines, reducing average processing time to generate analysis-ready data from 6 weeks down to a few days. Active board member and speaker for various panel discussions in S&P Global’s Women in Technology.

Answered technical questions in Python training for Machine Learning (ML) and NLP for 75+ data practitioners at Center For Disease Control and Prevention (CDC) live stream training. Guided trainees in breakouts with code exercises in real time and to proceed ML- and NLP-driven work-related projects.

Utilized machine learning and data science techniques to deliver actionable insights in global power generation in 10 geographical regions covering 190+ countries worldwide. Built an interactive dashboard using Dash Python to formulate energy market- and asset-level impacts on fossil fuel outlook. Developed high-performing ETL pipelines for power and utility data, decreasing average processing time from 10 days to under 10 minutes.

Collaborated with R&D team to develop RNN-based LSTM while applying denoising tools (i.e. Fast Fourier Transform) to detect anomalies in time series sensor data on athletes’ performance, improved detection performance by 55%.

Collaborated with cross-functional teams to develop and manage methodologies and algorithms on the KPIs of mutual fund and fund. Led the quantitative analysis on the US side of individual fund, fund families, and asset class/industry to identify trends and insights for investment-decision making.