Verified Expert in Engineering
Data Scientist and Quantitative Software Developer
Jesse is a machine learning and software engineer with 10+ years of versatile industry experience developing robust solutions for social networks, ads engineering, quantitative investments, and wireless systems. Jesse is passionate about delivering high-quality code and solutions that meet the needs and expectations of his clients. He enjoys collaborating with team members with a professional attitude and exploring new ideas and technologies.
Linux, Visual Studio Code (VS Code), GitHub
The most amazing...
...projects I've worked on are an ML pipeline for Reddit Ads Engineering, statistical models, and software for GPS chips in Samsung Galaxy and algorithmic trading.
Machine Learning Engineer
- Developed, updated, and maintained a feature engineering pipeline for various ads prediction models, including click-through and conversion models, with Google BigQuery, Apache Airflow, and dbt.
- Updated and maintained the ads inference server, modeling pipeline, and prediction interface with the upstream ads server according to various requirements and design changes.
- Developed ads modeling performance monitoring and tracking dashboards with Mode.com.
Senior Quantitative Researcher
- Researched cryptocurrency markets and statistical data analysis.
- Developed quantitative crypto trading models based on Bayesian statistical, machine learning (GBDT) algorithms, and signal filtering technologies.
- Developed data pipelines and quantitative model evaluation and backtesting systems.
Quantitative Researcher | Quantitative Developer
iFDC Capital Management, LLC
- Developed a data pipeline and management dashboard with Python for real-time data streaming, historical database query, model optimization and monitoring, statistics and indicators generation and reporting, and user front-end visualization.
- Led the quantitative software development of the event-driven algorithm module for a micro-service structured application. Implemented the cost and timing-optimized automatic execution algorithms and deployed them in the cloud.
- Developed the software implementation of the statistical arbitrage models and strategies for commodity futures portfolio management, cointegration testing, and dynamic hedging with a Bayesian filtering algorithm.
- Researched modeling with Conv1D, ResNet-1D, WaveNet, and fine-tuned pre-trained ResNet-2D models on CWT transformed signal to adapt the weights for volatility time series forecasting.
Staff Systems Engineer
- Developed the statistical models and algorithms for the highly accurate GPS receiver on-chip clock system.
- Analyzed a large amount of lab data for cellular radio chips and built statistical models to trade off between radio communication metrics. Developed numerical programs with Python to search circuit parameters and optimize chips' performance.
- Developed a comprehensive set of automation tools in Python for regression characterization. The tools manage testing cases, error recovery, lab instruments control, and data report.
- Developed a GPS receiver host software and signal processing algorithms in C++, log analysis and processing with Python, and software and algorithm defect tracking via Jira.
Ads Prediction 10x Features
Ads Prediction Batch Feature Engineering Pipeline
Ads Prediction Modeling Performance-tracking Dashboard
Quantitative Cryptocurrecy Trading Model
Algorithmic Trading Module of an Asset Management Platform
• Led the quantitative software development of the event-driven algorithm module for the micro-service structured proprietary asset management cloud application.
• Researched the convolutional deep learning models (Conv1D, ResNet, EfficientNet, WaveNet, DenseNet, etc.) applied on the CWT transformed signal for time series forecasting.
• Innovated algorithmic order types to reduce transaction cost and risk and optimized the leg synchronous execution for spread trading to reduce timing risk.
• Developed data pipelines and a portfolio management dashboard for real-time market data streaming, historical database query, model optimization and monitoring, statistics and indicators generation and reporting, and user front-end visualization.
Python, SQL, C++, Java, Go
Pandas, Scikit-learn, NumPy, PyTorch, Keras, SciPy, TensorFlow, Matplotlib, REST APIs
Machine Learning, Signal Processing, Quantitative Finance, Wireless Systems, Software Development, Finance, Financial Modeling, Stock Trading, Visualization, Statistics, Statistical Analysis, Software Engineering, Deep Learning, Data Modeling, Statistical Data Analysis, Bokeh, Data Visualization, Predictive Modeling, Artificial Intelligence (AI), Back-end Development, Data Build Tool (dbt), Semiconductors, GPS, MLflow, Machine Learning Operations (MLOps), Data Engineering, Google BigQuery, Dashboards, Stock Analysis, Stock Market, Data Analytics, Data Manipulation, Time Series Analysis, RESTful Microservices, Back-end, Distributed Systems, Ads, Statistical Modeling, Signal Filtering, Dash
MATLAB, Apache Airflow, GitHub
Data Science, ETL, Microservices
Jupyter Notebook, Docker, Visual Studio Code (VS Code), Blockchain, Linux, Amazon EC2, Amazon Web Services (AWS), Kubernetes
MySQL, Data Pipelines, ClickHouse
Ph.D. in Electrical Engineering
University of California, Riverside - Riverside, California, USA