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Brian Todd
Verified Expert in Engineering
Python Developer
New York, NY, United States
Toptal member since July 3, 2019
Bio
Brian is an experienced data scientist and machine learning engineer with a track record of researching and deploying a wide range of models for natural language processing (NLP) tasks, computer vision algorithms, classical machine learning algorithms, Bayesian statistical models, time series analysis models, and large-scale data mining algorithms.
Portfolio
Parabolica Labs
XGBoost, Natural Language Toolkit (NLTK), SpaCy, Dask, Databricks, Hadoop...
Twosense, Inc.
Amazon Web Services (AWS), Git, SciPy, Scikit-learn, Pandas, NumPy, SQL, Cython...
Skedaddle
SciPy, Scikit-learn, NumPy, Pandas, Snowflake, SQL, Flask, Python
Experience
- Python - 8 years
- Scikit-learn - 6 years
- SQL - 6 years
- NumPy - 6 years
- Pandas - 6 years
- Data Science - 6 years
- Natural Language Processing (NLP) - 4 years
- Computer Vision - 4 years
Work Experience
Data Scientist | Machine Learning Engineer Contractor
2019 - PRESENT
Parabolica Labs
- Provided machine learning and data science solutions with a specialization in natural language processing (NLP), time series analysis problems, applications of machine learning to sensor data, deep learning models, and Bayesian statistical models.
- Developed a production-grade conversational AI/chatbot for a Fortune 500 healthcare company (Rasa, spaCy) that allowed users to interact with their account and plans features through conversation.
- Performed in-depth analysis (SpaCy, NLTK, Textacy) and created data visualizations on large-scale text datasets to extract semantics, keywords, phrases, intents, and other linguistic features for product development and market exploration.
- Developed production models for classifying cohorts of users (Cython, Databricks, SciPy, Scikit-learn, XGBoost) using matrix factorization and prior behavioral data.
- Developed distributed data pipelines for processing and transforming large sets of financial time series data using Cython, C++, Dask, and Parquet.
Technologies: XGBoost, Natural Language Toolkit (NLTK), SpaCy, Dask, Databricks, Hadoop, Scikit-learn, Pandas, NumPy, SQL, Cython, Python
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 critical data 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.
- Completed feature extraction tasks from large, disparate datasets for model development.
Technologies: Amazon Web Services (AWS), Git, SciPy, Scikit-learn, Pandas, NumPy, SQL, Cython, Python
Senior Data Scientist
2017 - 2018
Skedaddle
- 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: SciPy, Scikit-learn, NumPy, Pandas, Snowflake, SQL, Flask, Python
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 based on accelerometer and biometric profiles using NumPy, SciPy, and scikit-learn.
Technologies: Amazon Web Services (AWS), SciPy, NumPy, Pandas, Scikit-learn, Redshift, TensorFlow, PyTorch, SQL, Python
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 clickstream data (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 clickstream data, market intent, and demographics.
- Built tools to monitor critical metrics and score production models daily using Python, MySQL, PostgreSQL, Presto, and MapReduce.
Technologies: Scikit-learn, NumPy, SciPy, Pandas, Keras, TensorFlow, SQL, Python
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: JavaScript, Python, PHP, MySQL, Apache, Linux
Project History
CyDTW
https://github.com/twosense/CyDTWHigh performance DTW library written in Cython for Python 3.x.
FX-Data-Loader
https://github.com/brian-todd/fx-data-loaderThis project serves as an extensible framework for processing and cleaning historical FX data from Dukascopy's data feed.
CFBBot
https://github.com/brian-todd/CFBBotChatbot built to answer questions about several college football teams. Built using Rasa, Spacy, and requests.
Patented Activity Recognition Algorithm
https://patents.google.com/patent/US10548513B2/A variety of techniques are used automate the collection and classification of workout data gathered by a wearable physiological monitor. The classification process is staged in order to correctly and efficiently characterize a workout type. Initially, a generalized workout event is detected using motion and heart rate data. Then a location of the monitor on a user is determined. An artificial intelligence engine can then be conditionally applied (if a workout is occurring and a suitable device location is detected) to identify the type of workout. In addition to improved speed and accuracy, a workout detection process implemented in this manner can be realized with a sufficiently small computational footprint for deployment on a wearable physiological monitor.
US Patent US10548513
US Patent US10548513
Education
2009 - 2013
Bachelor of Science Degree in Mathematics
University of Michigan - Ann Arbor, Michigan, USA
Skills
Libraries/APIs
LSTM, NumPy, SciPy, Pandas, Scikit-learn, PyTorch, XGBoost, Matplotlib, SpaCy, Natural Language Toolkit (NLTK), Dask, Keras, TensorFlow, OpenCV, D3.js
Tools
GitHub, Jupyter, Git, Seaborn, Rasa.ai, Sublime Text, Apache, Gensim, Apache Impala
Languages
Python, SQL, C, PHP, JavaScript, C++, Bash, Snowflake
Platforms
Jupyter Notebook, Linux, Amazon Web Services (AWS), Databricks, Docker, Unix, Google Cloud Platform (GCP), NVIDIA CUDA
Storage
PostgreSQL, MySQL, Redshift, SQLite, Apache Hive
Paradigms
Unit Testing, MapReduce
Frameworks
Flask, Hadoop, Presto, Spark
Other
Data Analysis, Data Visualization, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Gated Recurrent Unit (GRU), Data Analytics, Scientific Computing, Numerical Methods, Numerical Modeling, Data Science, Machine Learning, Computer Vision, Natural Language Processing (NLP), A/B Testing, Mathematics, Statistics, Deep Learning, Data Mining, Generative Pre-trained Transformers (GPT), Generative Artificial Intelligence (GenAI), Artificial Intelligence (AI), Cython, Graphics Processing Unit (GPU), GPU Computing, Google BigQuery
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