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Brian Todd, Natural Language Processing (NLP) Developer in New York, NY, United States
Brian Todd

Natural Language Processing (NLP) Developer in New York, NY, United States

Member since May 20, 2019
Brian is an experienced data science and machine learning expert. He has expertise in researching and deploying a wide range of deep learning models, classical machine learning algorithms, Bayesian statistical models, time series analysis models, and large scale data mining algorithms.
Brian is now available for hire


  • Twosense, Inc.
    Python, Cython, SQL, NumPy, Pandas, Scikit-learn, SciPy, AWS, Git
  • Skedaddle
    Python, Flask, SQL, Snowflake, Pandas, NumPy, Scikit-learn, SciPy
  • Whoop, Inc.
    Python, SQL, PyTorch, TensorFlow, SQL, AWS, Redshift, Scikit-learn, Pandas...


  • Python, 8 years
  • SQL, 6 years
  • Data Science, 5 years
  • NumPy, 5 years
  • Pandas, 5 years
  • Scikit-learn, 5 years
  • Computer Vision, 4 years
  • Natural Language Processing (NLP), 4 years
New York, NY, United States



Preferred Environment

Linux, Git, AWS, Sublime Text, Bash

The most amazing...

...project I've developed was a real-time deep learning pipeline that detected and classified user actions based on sensor data from a wrist-worn device.


  • Machine Learning Engineer

    2018 - 2019
    Twosense, Inc.
    • Researched, developed, and deployed a suite of machine learning models (NumPy, SciPy, scikit-learn, XGBoost, Cython) that authenticated users based on behavioral biometrics collected from sensors on phones and computers.
    • Wrote large-scale data processing scripts that consumed real-time biometric data for model training and testing using NumPy, AWS Redshift, and Pandas.
    • Produced Jupyter notebooks visualizing model validation metrics, data transformations, and feature analysis.
    • Guided best practices, led technical sessions, collaborated on project specifications, and wrote significant amounts of research documentation. I was hired as the first member of the machine learning and data science team.
    • Wrote suites of unit tests for data processing, feature, extraction, and model validation.
    Technologies: Python, Cython, SQL, NumPy, Pandas, Scikit-learn, SciPy, AWS, Git
  • Senior Data Scientist

    2017 - 2018
    • Developed a production-grade API for pricing algorithms using NumPy, scikit-learn, Lambda, and API Gateway.
    • Built and maintained complete data pipeline platform reading public APIs using EC2, Lambda, and Snowflake, for creating time series models predicting product demand.
    • Wrote a serverless web app displaying data visualizations and real-time monitors of key metrics using Flask, Zappa, and D3.js.
    • Provided ad hoc analysis for all domains within the organization, and guided other team members in their analyses.
    Technologies: Python, Flask, SQL, Snowflake, Pandas, NumPy, Scikit-learn, SciPy
  • Senior Data Scientist

    2016 - 2017
    Whoop, Inc.
    • Led team code reviews and collaborated on the direction of quarterly team goals and projects.
    • Researched, developed, and deployed novel, deep, convolutional neural networks for classifying activities based on multidimensional sensor data using PyTorch.
    • Developed and maintained data pipelines consuming from Redshift and PostgreSQL databases.
    • Built a real-time activity detection algorithm for biometric time series data using NumPy and SciPy.
    • Researched and developed convolutional autoencoder models for compressive sensing using PyTorch.
    • Wrote a real-time algorithm to detect how the user wears a sensor packet based on accelerometer and biometric profiles using NumPy, SciPy, and scikit-learn.
    Technologies: Python, SQL, PyTorch, TensorFlow, SQL, AWS, Redshift, Scikit-learn, Pandas, NumPy, SciPy
  • Associate Data Scientist

    2014 - 2016
    Cogo Labs
    • Implemented a Python library for A/B testing with Bayesian Statistics and other measurement tools.
    • Developed statistical methods to mine URLs from user web browsing history (Presto) and developed neural networks (Tensorflow, Keras) to model user level characteristics from the browsing history.
    • Applied NLP (scikit-learn, NumPy) techniques to cluster ad campaigns based on content similarity.
    • Wrote algorithms (NumPy, SciPy, Pandas, scikit-learn) for user-campaign selection and developed models based on engagement, market intent, and demographics.
    • Built tools to monitor critical metrics and score production models daily using Python, MySQL, PostgreSQL Presto, and MapReduce.
    Technologies: Python, SQL, TensforFlow, Keras, Pandas, SciPy, NumPy, Scikit-learn
  • Full-stack Developer

    2013 - 2014
    Microsoft Project Users Group
    • Maintained the existing infrastructure and developed new back-end features and APIs on the legacy LAMP (PHP) stack.
    • Performed front-end work using HTML5, CSS, and JavaScript to improve user experience and fix legacy bugs.
    • Wrote Python scripts to automate data processing and reporting from third-party APIs and internal database sources.
    Technologies: Linux, Apache, MySQL, PHP, Python, JavaScript



  • Languages

    Python, SQL, C++, Bash, Snowflake
  • Libraries/APIs

    NumPy, SciPy, Pandas, Scikit-learn, PyTorch, XGBoost, Matplotlib, TensorFlow, OpenCV, SpaCy, NLTK
  • Tools

    Git, Seaborn, Gensim
  • Paradigms

    Data Science, Unit Testing
  • Storage

    PostgreSQL, MySQL, Redshift, SQLite
  • Other

    Machine Learning, Computer Vision, Natural Language Processing (NLP), A/B Testing, Cython, Google BigQuery
  • Platforms

    Linux, Amazon Web Services (AWS), Unix
  • Frameworks

    Flask, Hadoop, Presto DB


  • Bachelor of Science degree in Mathematics
    2009 - 2013
    University of Michigan - Ann Arbor, Michigan, USA
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