Ryan is available for hire

Ryan Tang

Verified Expert in Engineering

Statistics Developer

Location

Durham, NC, United States

Toptal Member Since

January 21, 2022

Ryan is a data-driven professional specializing in Bayesian statistics, machine learning, and real-time algorithm design. He brings years of interdisciplinary experience in statistics and machine learning with a sprinkle of business acumen from diverse industries in tech, advertising, real estate, finance, and insurance. Ryan has a fastidious ability to solve complex technical problems to reach desired results through novel ideas and rigorous approaches.

Data Analytics Machine Learning Data Visualization Statistical Learning Data Engineering Distributed Systems Python SQL Pandas NumPy ETL BigQuery Big Data Analytics Algorithms

Portfolio

Reddit, Inc.

Data Science, Distributed Systems, Software Engineering, Go, Scala, Python...

Duke University | Department of Statistics

Python, Algorithms, Machine Learning, Statistics, Bayesian Statistics...

Ridge Equities

Python, Dashboards, Statistics, Machine Learning, Business Intelligence (BI)...

Experience

Statistics - 10 years Python - 10 years Statistical Modeling - 8 years Data Engineering - 8 years Machine Learning - 8 years Data Science - 8 years Time Series Analysis - 6 years Bayesian Statistics - 3 years

Availability

Part-time

Preferred Environment

Visual Studio Code (VS Code), Jupyter Notebook, Python, Git

The most amazing...

...project I've developed is a fully distributed event-driven back-testing system that has led to my Sharpe 2.0+ quantitative market-neutral strategy.

Work Experience

Senior Machine Learning Engineer

2022 - 2023

Reddit, Inc.

Led and contributed to Reddit's auto-bidding strategies. Worked on designing and implementing the core algorithm in a distributed, real-time environment.
Incremental improvements in revenue of 2.5%, budget utilization of 12%, and 30% clicks.
Provided technical leadership in algorithms and infrastructure behind the entire auto-bidding strategies.
Took ownership of Maximize Clicks v2, Maximize Clicks v2.5 and Max Clicks Lagrangian.
Performed rigorous experiment design and statistical validation throughout.
Spearheaded distributed processing of over terabytes each day.

Technologies: Data Science, Distributed Systems, Software Engineering, Go, Scala, Python, Java, Spark, BigQuery, ETL, Mathematics, Quantitative Analysis, Numerical Analysis, Algorithms, Back-end Development, Machine Learning, Optimization, Statistics, Statistical Modeling, Bayesian Statistics, Bayesian Inference & Modeling, Real-time Streaming, Real-time Systems, Real-time Bidding (RTB), Experimental Design, Causal Inference, Reinforcement Learning, Docker, Amazon Web Services (AWS), GitHub, Advertising, Event-driven Programming, Time Series Analysis, Data Engineering, NumPy, Pandas, Data Analytics, Statistical Learning, Linear Programming, SQL, Data Visualization, Distributed Computing, Data Pipelines, Computational Advertising, Linear Algebra, Object-oriented Programming (OOP), Visual Studio Code (VS Code), Jupyter Notebook, Git, Scikit-learn

Research Scientist

2021 - 2022

Duke University | Department of Statistics

Utilized statistical and machine learning knowledge to develop new methodologies while improving the existing state-of-art ones.
Conducted research aligned with recent field developments and literature. Implemented qualitative and quantitative analysis and data collection tools to achieve the assigned tasks within specified periods.
Assisted the team in conducting intensive data analysis at MovieLens 25M datasets that explore people's movie rating behaviors from multiple lenses.
Finalized and submitted research results to the group with recommendations on specific topics. Accomplished a seven-page write-up, supporting the team a step closer to the goal of publishing a paper.

Technologies: Python, Algorithms, Machine Learning, Statistics, Bayesian Statistics, Recommendation Systems, Computational Advertising, Research, Mathematics, PostgreSQL, Data Science, NumPy, Pandas, SQL, Data Engineering, Quantitative Analysis, Distributed Systems, ETL, Numerical Analysis, Ads, Advertising, GitHub, Git, Data Analytics, Statistical Learning, Statistical Modeling, Experimental Design, Causal Inference, Reinforcement Learning, Software Engineering, Time Series Analysis, Linear Programming, Data Visualization, Data Pipelines, Linear Algebra, Object-oriented Programming (OOP), Visual Studio Code (VS Code), Jupyter Notebook, Bayesian Inference & Modeling, Scikit-learn

Principal

2015 - 2021

Ridge Equities

Spearheaded private equity fund operations, optimizing operational efficiency through systematized market operations and strategy development for a single-family value-add rental investment.
Standardized business operations, value-add capital improvement projects, budget and timeline controls, trade coordination, and quality control assurance compliance with policies or regulations.
Expanded business opportunities by directing a total asset of over $5 million, capitalizing on management and excellent communication skills to convey a consistent annual equity return of more than 15%.
Bolstered operations, revenue generation, and client base expansion by instituting innovative portfolio management strategies for over 33 units across Philadelphia Metro.
Executed comprehensive property management, incorporating best practices in tenant screening, repair and maintenance, cost control, rent collection, dispute handling, and capital improvement to meet optimal equity and internal rate returns.
Boosted strategic leadership and communication among stakeholders and cross-functional teams, instilling the company vision to influence business transformation and meet objectives.

Technologies: Python, Dashboards, Statistics, Machine Learning, Business Intelligence (BI), Asset Management, Equity Investment, Asset Valuation, Leadership, Property Management, Private Equity, Wealth Management, PostgreSQL, Dash, Quantitative Analysis, Algorithms, WebApp, Flask, Back-end Development, Data Science, Git, GitHub, Data Analytics, Statistical Learning, Statistical Modeling, Back-end, Pandas, NumPy, SQL, Data Engineering, Experimental Design, Causal Inference, Algorithmic Trading, Event-driven Programming, Numerical Analysis, Software Engineering, ETL, Time Series Analysis, Data Visualization, Data Pipelines, Linear Algebra, Object-oriented Programming (OOP), Mathematics, Visual Studio Code (VS Code), Jupyter Notebook, Scikit-learn

Senior Data Scientist

2016 - 2017

Guardian Insurance

Developed the company's first customer segmentation model about life insurance purchasers' key life events and behavior drivers, utilizing extensive statistics modeling and pulling data from a large volume of datasets from various sources.
Achieved an average of 1.6 times of target segment lifts, reducing the client acquisition cost and improving conversation rate to optimize the overall marketing profit and loss (P&L).
Amplified the AUC metric by over 8% by introducing nonlinearity with additional critical behavior features into the prospect-predicting model.

Technologies: Python, Analytics, Business Intelligence (BI), Hadoop, Spark, Machine Learning, Customer Segmentation, Cross-selling, Upselling, Statistics, PostgreSQL, Oracle, PySpark, MapReduce, Data Pipelines, Distributed Computing, NumPy, Pandas, Data Engineering, SQL, Data Science, Distributed Systems, Software Engineering, BigQuery, ETL, Tableau, Quantitative Analysis, Numerical Analysis, Algorithms, Git, GitHub, Back-end, Amazon Web Services (AWS), Docker, Data Analytics, Statistical Learning, Statistical Modeling, MySQL, MongoDB, Causal Inference, Experimental Design, Event-driven Programming, Linear Programming, Data Visualization, Bayesian Statistics, Linear Algebra, Object-oriented Programming (OOP), Mathematics, Visual Studio Code (VS Code), Jupyter Notebook, Scikit-learn

Business Analyst

2014 - 2016

Guardian Insurance

Established rich interactive visualizations through data interpretation and analysis to integrate multiple data sources to support performance analysis, agency and producer ranking and awards, and internal marketing strategy.
Evaluated data collection processes for various business reports, utilizing multiple datasets to develop visual displays of solutions. Communicated data analysis results in written and verbal form for a more effective presentation.
Strategized business intelligence solutions by updating the latest information technology applications. Automated over 80% of department internal ad-hoc reports using Python, Tableau, Excel, and VBA.

Technologies: Python, Statistics, Analytics, Business Intelligence (BI), Dashboards, Excel 365, Excel VBA, Tableau, PostgreSQL, Oracle, Data Visualization, Data Pipelines, Data Cleaning, Data Scraping, SQL, Data Engineering, NumPy, Pandas, Data Science, Quantitative Analysis, ETL, Algorithms, Numerical Analysis, Git, GitHub, Back-end, Data Analytics, Statistical Learning, Statistical Modeling, Software Engineering, Linear Algebra, Object-oriented Programming (OOP), Mathematics, Visual Studio Code (VS Code), Jupyter Notebook, Machine Learning, Scikit-learn

Operation Research Consultant

2015 - 2015

Gemological Institute of America

Supervised more than three professionals in a supply chain optimization project to streamline the internal quality control logistic system.
Theorized the logistics system using linear programming and proposed a route for production implementation. Provided a full-size demo on Python and Django frameworks focused on online learning.
Formulated an operational strategy, mapped a value chain, and conducted quantitative research for prospective institute models.

Technologies: Python, Django, Operations Research, Linear Programming, Optimization, Research, Data Science, Data Engineering, SQL, MySQL, NumPy, Pandas, Machine Learning, Quantitative Analysis, Numerical Analysis, Algorithms, Back-end, Back-end Development, Git, GitHub, Data Analytics, Statistical Learning, Statistical Modeling, Software Engineering, ETL, Data Visualization, Linear Algebra, Object-oriented Programming (OOP), Statistics, Mathematics, Visual Studio Code (VS Code), Jupyter Notebook, Scikit-learn

Experience

Equity Investment Web App

This is a Streamlit-powered data application for value investment research on stocks. The ultimate purpose of this app is to provide comprehensive fundamental data to make informed investment decisions. It consists of the competitor analysis, debt and leverage analysis, operational efficiency, return on investment (ROI), return on equity (ROE), and cash flow.

Distributed Event-driven Backtesting System

A pythonic event-driven backtesting system was used to analyze my quantitative strategies. It has a component that handles slippage and order executions, a portfolio manager that rebalances between multiple concurrent strategies, and an extensive backtesting analytics component for in-depth research.

Manhattan College Business Analytics Competition | First Place

https://manhattan.edu/news/archive/2015/05/first-annual-business-analytics-conference-and-competition-explores-art-and-science-decision

The events featured industry leaders and included an exciting opportunity for undergraduate students studying business analytics or related fields to test their knowledge and develop their skills. Competing students engaged in the “art and science” of decision-making while practicing their ability to draw business insights through comprehensive analyses of data in creative ways. My team and I, as a team lead, won first place in this competition.

Skills

Languages

Python, SQL, Scala, Excel VBA, Go, Java

Libraries/APIs

Pandas, NumPy, Scikit-learn, PySpark

Tools

Git, Tableau, BigQuery, GitHub

Paradigms

Object-oriented Programming (OOP), Unit Testing, Business Intelligence (BI), Distributed Computing, Linear Programming, Data Science, ETL, Event-driven Programming, Real-time Systems, Dynamic Programming, MapReduce

Platforms

Jupyter Notebook, Oracle, Docker, Visual Studio Code (VS Code), Amazon Web Services (AWS)

Storage

PostgreSQL, Data Pipelines, MySQL, MongoDB

Other

Operations Research, Mathematics, Statistics, Big Data, Analytics, Algorithms, Linear Algebra, Partial Differential Equations, Principal Component Analysis (PCA), Optimization, Stochastic Gradient Descent (SGD), Machine Learning, Bayesian Statistics, Recommendation Systems, Computational Advertising, Research, Dashboards, Asset Management, Equity Investment, Asset Valuation, Private Equity, Wealth Management, Customer Segmentation, Excel 365, Data Visualization, Data Cleaning, Statistical Learning, Data Analytics, Data Engineering, Financial Engineering, Competitor Analysis & Profiling, Time Series Analysis, Distributed Systems, Software Engineering, Quantitative Analysis, Numerical Analysis, Algorithmic Trading, Statistical Modeling, Reinforcement Learning, Bayesian Inference & Modeling, Experimental Design, Real-time Streaming, Real-time Bidding (RTB), Graph Theory, Leadership, Property Management, Cross-selling, Upselling, Dash, Data Scraping, Streamlit, APIs, Ads, Advertising, Back-end, Causal Inference, Natural Language Processing (NLP), Signal Processing, Back-end Development, Game Development, Artificial Intelligence (AI), GPT, Generative Pre-trained Transformers (GPT)

Frameworks

Hadoop, Spark, Django, WebApp, Flask

Education

2022 - 2023

Master's Degree in Statistical Science

Duke University - Durham, NC, United States

2011 - 2015

Bachelor's Degree in Business Analytics

Pace University - New York, NY, United States

Certifications

JANUARY 2022 - PRESENT

Reinforcement Learning Specialization

Coursera

NOVEMBER 2021 - PRESENT

Fundamentals of Computing Specialization

Coursera

OCTOBER 2021 - PRESENT

Mathematics for Machine Learning Specialization

Coursera