Christopher Maierle, Software Developer in Corvallis, OR, United States
Christopher Maierle

Software Developer in Corvallis, OR, United States

Member since September 29, 2020
Chris Maierle is a seasoned data scientist, trader, and researcher. He has extensive experience applying and developing quantitative and machine learning models in both finance and academic contexts. Recently, Chris worked at XR Trading as a high-frequency trader, and before that was a quantitative analyst at Goldman Sachs.
Christopher is now available for hire




Corvallis, OR, United States



Preferred Environment

Python, Qt, SQL, R, C++, Jupyter Notebook, MacOS, Windows, Linux

The most amazing...

...thing I've developed is a high-performance and flexible plotting tool for visually inspecting high frequency time series data.


  • Trader | Data Scientist

    2015 - 2019
    XR Trading
    • Researched, developed, and implemented high-frequency trading strategies using Python, R, and C++.
    • Tracked issues and fixed them using Atlassian Jira, Git repositories, and unit testing.
    • Developed and optimized machine learning pipelines using supervised and unsupervised learning.
    • Created an interactive high-performance plotting tool for graphically investigating high-frequency time-series data.
    • Parsed binary messages to extract data and insights supporting the business.
    • Gave regular presentations on the research status and current business activities.
    Technologies: Cython, C, Visualization Tools, Visualization, Test-driven Development (TDD), Futures & Options, Futures, Forex, Forex Trading, Stock Trading, Option Pricing, Options Trading, Options Theory, Trading Systems, Trading, Quantitative Research, Quantitative Finance, Quantitative Analysis, Excel 2013, Excel 2016, Data Analysis, Predictive Analytics, Statistical Data Analysis, Statistical Analysis, Statistical Modeling, Statistics, Data Analytics, Data Modeling, Bokeh, Matplotlib, Pandas, Data Visualization, Data Science, Microsoft Excel, Microsoft PowerPoint, Wireshark, Scikit-learn, PyQt, Qt, R, Jira, Git, Python 2, Python 3, C++11
  • Strategist

    2007 - 2014
    Goldman Sachs
    • Managed the pricing and risks for products including equity, rates, FX, and credit components.
    • Implemented model valuation tests and documentation in compliance with federal regulations.
    • Developed the firm’s first ALM model covering the global equity division.
    • Implemented electronic connections to Bovespa for the automation of stock-lending trades.
    Technologies: Equity Derivatives, Derivatives, Test-driven Development (TDD), Finance, Trading Applications, Futures, Futures & Options, Option Pricing, Options Trading, Options Theory, Trading, Trading Systems, Quantitative Analysis, Quantitative Modeling, Quantitative Finance, Quantitative Research, Equity Financing, Equity Market Data, Financing, Excel 2016, Excel 2013, Data Analysis, Data Analytics, Statistical Modeling, Data Modeling, Statistics, Data Visualization, Data Science, Relational Databases, SQL, Bloomberg API, Bloomberg, Microsoft Excel, Slang (Custom Computer Language)
  • Chemistry Lecturer

    2004 - 2006
    University of of San Francisco, SF State University, Diablo Valley College, Laney College
    • Planned and presented lectures for college-level chemistry classes.
    • Prepared and performed engaging and interactive demonstrations.
    • Coordinated with teaching assistants and laboratory staff.
    Technologies: Organic Chemistry, Chemistry, University Teaching, Microsoft Excel, Microsoft PowerPoint


  • Data Wrangling Projects

    In past roles, a large part of my day-to-day work centered around creating processes that present data in a useful form. In these projects, the end form might be a simple report, a real-time dashboard, or an API or process that exposes data for further analysis in Python, R, or BI software.

    No matter what the end product is, the process starts with understanding the value the business expects to extract from data as well as any expected challenges or known deficiencies in the data. As a second step, I establish access to the data in R or Python and start exploring. I'm looking to see how complete the data is, whether there appear to be errors, and most importantly, am looking to see that the data shows the trends or relationships expected by the business.

    The final step is to set up reports, dashboards, or APIs that expose the data. Usually, this will involve creating intermediate processes to clean, transform or process the data as well as setting up monitoring to make sure that the intermediate processes continue to function properly in the background.

  • High Performance Time Series Plotting Tool

    I used and extended the PyQtGraph library to develop a flexible plotting tool capable of interactively displaying ~100,000 data points. It allows for seamless zoom from minute to microsecond timescale and incorporates data augmentation features such as the ability to click on points to display additional information.

  • Efficient Custom Loaders for Binary Data

    I developed code to create highly performant custom data loaders to load arbitrary binary data to Pandas/NumPy in Python. The techniques developed also support high performance loading of irregularly structured text data.

  • Kaggle Contests

    I've competed in a few Kaggle contests and done well in all of them. I finished individually in the top 5% globally in the 2019 Data Science Bowl, and my team and I finished in the top 10% in the TensorFlow 2.0 Question Answering contest. The 2019 Data Science Bowl involved semi-structured data and traditional machine learning techniques such as gradient boosted trees, bagging, and feature selection and engineering. In the TensorFlow 2.0 contest, my team and I applied the Bert NLP model to a question answering dataset.


  • Languages

    C++, C++11, R, Python, Python 3, Python 2, C, SQL, Bash Script, HTML, JavaScript
  • Frameworks

  • Libraries/APIs

    PyQt, Scikit-learn, Pandas, Matplotlib, Bloomberg API, TensorFlow, PyTorch
  • Paradigms

    Data Science, Quantitative Research, Test-driven Development (TDD)
  • Other

    Financial Modeling, Quantum Computing, Quantitative Modeling, Research, Physics, Chemistry, University Teaching, Mathematics, Machine Learning, Data Visualization, Feature Analysis, Predictive Analytics, Data Analysis, Quantitative Analysis, Quantitative Finance, Trading, Options Theory, Options Trading, Option Pricing, Futures, Equity Market Data, Equity Financing, Trading Applications, Visualization, Derivatives, Equity Derivatives, Monte Carlo Simulations, Organic Chemistry, Statistics, Data Modeling, Statistical Modeling, Data Analytics, Statistical Analysis, Statistical Data Analysis, Web Scraping, Scraping, Stock Trading, Forex Trading, Forex, Financing, Time Series Analysis, Biology, Geology, Computer Science, Cython, Deep Learning, Natural Language Processing (NLP), Bokeh, Futures & Options, Finance, Visualization Tools, APIs
  • Tools

    Git, Microsoft PowerPoint, Microsoft Excel, Excel 2016, Jira, Wireshark, Bloomberg, Excel 2013
  • Platforms

    Linux, Jupyter Notebook, Windows, MacOS, Amazon Web Services (AWS)
  • Storage

    Relational Databases, Google Cloud
  • Industry Expertise

    Trading Systems


  • Master's Degree in Financial Engineering
    2006 - 2007
    University of California, Berkeley—Haas School of Business - Berkeley, CA, United States
  • Ph.D. in Theoretical Chemistry
    1995 - 1999
    University of California, Berkeley - Berkeley, CA, United States
  • Bachelor of Arts Degree in Integrated Science Program
    1991 - 1995
    Northwestern University - Evanston, IL, United States

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