Jesse Liu
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.
Portfolio
Experience
Availability
Preferred Environment
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.
Work Experience
Machine Learning Engineer
Reddit, Inc.
- Developed a model training pipeline for ads prediction models utilizing gradient-boosting decision trees (GBDTs) and bid adjustment architecture, implemented using TensorFlow Decision Forests (TF-DF).
- Built a feature engineering pipeline leveraging Google BigQuery, Apache Airflow, and dbt to handle daily roll-up of PB-level big data, feature generation, ingestion, and training/test dataset creation for ads CPC and CPA cost models.
- Constructed ads prediction modeling performance monitoring and tracking dashboards on Mode, which effectively extracted business metrics, e.g., conversion rate, prediction rate, calibration error, conversion distribution, revenue/ARPU, CPM, and more.
- Tracked and evaluated ads conversion modeling performance with segmentation analysis on various features, including placement/platform types, advertiser industry, user privacy flags, and sales channel, based on A/B tests and DDG experiments results.
- Developed and maintained the prediction API between the ads inference server and upstream requesting server, ensuring seamless integration and responsiveness to model updates, requirements, and design changes.
Senior Quantitative Researcher
Outremont Technologies
- Performed a thorough statistical analysis and evaluation of all-events cryptocurrency market data, examining the efficacy of quantitative signals and features provided by various vendors.
- Conducted in-depth research on novel deep learning approaches for time series forecasting, including gradient boosting tree, N-Beats and TFT transformer models, assessing their potential to enhance crypto portfolio performance.
- Investigated the application of convolutional deep learning architectures to transformed signals, evaluating their effectiveness in momentum and volatility time series forecasting for crypto assets.
- Developed a robust break-out momentum trading signal generation framework, employing probabilistic filtering and transaction cost analysis techniques to identify opportunities in the cryptocurrency market.
Quantitative Researcher | Data Scientist
iFDC Capital Management, LLC
- Leveraged statistical data analysis, trading signal hypothesis testing and optimization, and quantitative strategies to enhance financial derivatives portfolio management.
- Developed time series regression models for parameter estimation of mean-reversion stochastic process models, contributing to the refined stock index investment strategies.
- Created a data pipeline and management dashboard, enabling real-time data streaming, historical data query, model monitoring, performance reporting, and visualization.
- Established robust research databases for all-events historical market data using Clickhouse, providing a centralized repository for data-driven analysis and strategy development.
Staff Systems Engineer
Broadcom
- 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.
Experience
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.
Skills
Languages
Python, SQL, C++, Go, HTML
Libraries/APIs
Pandas, Scikit-learn, NumPy, PyTorch, Keras, SciPy, TensorFlow, Matplotlib
Paradigms
Data Science, Quantitative Research, ETL, Microservices
Other
Machine Learning, Signal Processing, Quantitative Finance, Wireless Systems, Software Development, Finance, Financial Modeling, 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, Data Analytics, Data Manipulation, Quantitative Analysis, Forecasting, Regression, Classification, Data Analysis, Algorithms, Rankings, Recommendation Systems, Advertising Technology (Adtech), Convolutional Neural Networks (CNN), Time Series Analysis, Back-end, Distributed Systems, Ads, Statistical Modeling, Signal Filtering
Tools
MATLAB, Apache Airflow, GitHub
Platforms
Jupyter Notebook, Docker, Visual Studio Code (VS Code), Google Cloud Platform (GCP), Linux, Amazon EC2, Amazon Web Services (AWS)
Storage
MySQL, Data Pipelines, ClickHouse, PostgreSQL
Education
Ph.D. in Electrical Engineering
University of California, Riverside - Riverside, California, USA
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