Jesse Moore, Developer in Calgary, AB, Canada
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Jesse Moore

Verified Expert  in Engineering

Data Scientist and Software Developer

Location
Calgary, AB, Canada
Toptal Member Since
June 4, 2019

Jesse is an experienced data scientist, CTO, and founder who solves complex problems. He has founded four companies, including Sigmai, an automated news parsing company for hedge funds that was acquired in 2018; Mobilads, reaching an annual run rate of $5 million a year; Bluescribe, a bi-directional English and French translation engine for Canadian legal documents; and Relu Analyticsa, a data-science consulting company. He is currently at ThinkAlpha Securities as head of data science.

Portfolio

THINKalpha
Python 3, Amazon Web Services (AWS), SpaCy, Natural Language Processing (NLP)...
Voiceops
Document Processing, Custom BERT, Data Science, Deep Learning...
Mobilads
Data Science, Amazon Web Services (AWS), Python, Data Visualization...

Experience

Availability

Part-time

Preferred Environment

GitHub, Sublime Text, Linux, Python, Amazon Web Services (AWS), Artificial Intelligence (AI)

The most amazing...

...thing I've built is a deep-learning algorithm to detect market-moving events in the stock market. We were able to help hedge funds boost their returns.

Work Experience

Head of Data Science/Quantitative Trading

2020 - PRESENT
THINKalpha
  • Designed, built, and managed a quantitative trading engine that covers global equities, currencies, and cryptocurrencies. This system was used to build and optimize trading strategies that traded hundreds of millions of capital.
  • Built and integrated the ETL pipelines, monitoring, and code for the quantitative database that housed all market data across supported assets and integrated this with systems for backtesting and live-trading agents.
  • Created a natural language to quant-formula translation engine that generates quantitative trading strategies from verbal descriptions into formulas that can be backtested or traded in ThinkAlpha's trading engine.
  • Built, deployed, and optimized a variety of custom trading strategies for Avatar traders.
  • Designed, constructed, and managed a series of high Sharpe trading strategies.
Technologies: Python 3, Amazon Web Services (AWS), SpaCy, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Machine Learning, SQL, Data Visualization, Data Engineering, Statistical Analysis, Cloud, Real-time Data, Statistics, Predictive Analytics

Deep Learning Engineer

2019 - 2021
Voiceops
  • Developed the architecture and construction of AWS-based infrastructure for large-scale machine learning. VoiceOps is an AI-driven coaching and training platform for call centers.
  • Built DL models to support the transcription process. Included scripts to pre-train, fine-tune, and fully integrate transformers (e.g., BERT, various Hugging Face transformers) into novel new architectures that included both text and statistical data.
  • Built a modified transformer to automatically score the quality of transcriptions and determine whether they should pass to the client (ROC-AUC = 0.90).
  • Created a modified transformer that automated the detection of speakers based on text (ROC-AUC = 0.97).
  • Automated the estimation of how long a transcript would take to transcribe to replace a fixed-price system (cost savings of 20–30% of total transcription costs).
  • Improved Automated Speech Recognition (ASR) via Seq2seq architectures.
Technologies: Document Processing, Custom BERT, Data Science, Deep Learning, Amazon Web Services (AWS), PyTorch, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Machine Learning, Python, Keras, Fairseq, Artificial Intelligence (AI), SQL, Data Engineering, Statistical Analysis, Cloud

Chief Technology Officer

2019 - 2021
Mobilads
  • Constructed a geospatial system that maps physical ad impressions based on vehicle and mobile GPS data. The Mobilads geospatial system was successfully built to operate worldwide and to scale to thousands of vehicles and billions of GPS points.
  • Developed automated reporting systems for the clients of Mobilads to demonstrate the technology.
  • Built up the company's IP portfolio by integrating census, geotracking, and social data to enrich what Mobilads knows about the people who see their vehicles. This ensures consistent industry-leading return on ad spend.
  • Architected and led the development of Mobilads' app for autonomously managing tens of thousands of drivers.
Technologies: Data Science, Amazon Web Services (AWS), Python, Data Visualization, Data Engineering, Statistical Analysis, Cloud, Real-time Data

Founder, CEO, and Principal Consultant

2016 - 2021
Relu Analytics
  • Consulted as the senior data scientist at Step Energy Services. Built algorithms for optimizing the use of fixed equipment, including extended maintenance, failure prediction, forecasting, and budgeting, as well as cash flow prediction.
  • Worked with the leadership team of Cinelytics to build scalable NLP pipelines. Provided code samples and walked through the software engineering team on building and deploying deep learning models in the capacity of a data scientist at Cinelytic.
  • Designed an end-to-end machine learning application using Google Cloud to serve as an API for the front-end team to deliver predictions via the company's UI. Consulted as the data scientist at Meditalente GMBH.
Technologies: Document Processing, Custom BERT, Web Scraping, Data Science, Deep Learning, Amazon Web Services (AWS), PyTorch, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Machine Learning, Keras, Scikit-learn, Python, Artificial Intelligence (AI), SQL, Data Visualization, Data Engineering, Statistical Analysis, Google Cloud Platform (GCP), Cloud, Real-time Data, Predictive Analytics

CEO | Previously Chief Data Scientist

2017 - 2019
Sigmai
  • Led a team of 15 data scientists, linguists, software engineers, product managers, and sales professionals leading to Sigmai's acquisition in 2018 by Commetric.
  • Focused primarily on deep learning for text classification with Keras and TensorFlow and its integration within a rule-based NLP system.
  • Developed an out-of-memory document clustering system to allow the clustering of billions of news articles.
  • Built a natural language processing (NLP) system that rivaled the best NLP companies in finance and led to data trials with some of the largest fund managers.
  • Led and oversaw the Newsful application (app.Newsful.io) that was shortlisted for the 2018 SIIA CODiE Award. The business operations were acquired by Commetric.
Technologies: Document Processing, Custom BERT, Data Science, Deep Learning, Amazon Web Services (AWS), Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Machine Learning, R, Keras, Python, Artificial Intelligence (AI), Data Engineering, Statistical Analysis, Cloud, Statistics, Predictive Analytics

Data Scientist

2016 - 2018
Zalando
  • Developed analytical tools and ETL pipelines in Spark on AWS.
  • Built predictive tools for targeting audiences for specific ad campaigns.
  • Developed interactive data applications for product owners using Python and R Shiny to automate time-consuming analysis tasks, including customer journeys and return on ad spend.
  • Developed a system to optimize how ads are placed within the search and recommendation engine to reduce lost revenue due to poor ad placement by up to $0.5 million USD per month.
  • Designed a system for determining the causal impact of multiple concurrent ad campaigns, including off-site, on-site, banner Ads, and full-page ads, using regression and Bayesian time-series models.
Technologies: Amazon Web Services (AWS), Data Science, Machine Learning, Scikit-learn, R, Python, SQL, Data Engineering, Statistical Analysis, Cloud, Statistics, Predictive Analytics

NHL Systematic Betting

The goal for this project from the outset was to predict the outcome of hockey games and attempt to find systematic errors in betting odds by traditional providers such as Bet365 and Pinnacle.

The system began to approach parity in capability with the best provider in January 2019, and surpass it in February.

Put into production in March 2019 and successfully traded throughout the following 12 months, the system has returned over 900% to investors.

Newsful

https://www.youtube.com/watch?v=84KyTm3Xecc&t=85s
Sigmai’s proprietary NLP technology enabled our customers to be the first to act on breaking news and allowed us to build an unbelievable archive of corporate history.

Newsful was a demonstration of that technology, and was shortlisted for a CODiE Award in the Best Business Intelligence Reporting & Analytics category.

Sigmai: Skynet Natural Language Processing System

Lead author of a proprietary end-to-end deep-learning-based NLP system for classifying text.

Achieved state-of-the-art results for entity-based classification (classifying text as it relates to a specific entity in the text).

KISS Classifier

Leveraging the growing ability to develop world-class natural language processing (NLP) algorithms using transfer learning in a fraction of time, I wanted to create the simplest classifier I could using the fewest lines of code possible. The goal of this classifier was to have it perform without any customization required by the user aside from testing various hidden layer sizes.

BERT in Natural Language Processing (Talk)

https://www.youtube.com/watch?v=4Z_TzZJ-v3o
I explain "Using BERT To Accelerate Natural Language Processing". Data collection is burdensome, time-consuming, expensive, and the number one limiting factor for successful NLP projects. Preparing data, building resilient pipelines, making choices amongst hundreds of potential preparation options, and getting "model ready" can easily take months of effort even with talented machine learning engineers. Finally, training and optimizing deep learning models require a combination of intuitive understanding, technical expertise, and an ability to stick with a problem. Google's BERT (Bidirectional encoder representations from transformers) form transfer learning that requires far less data and training time. Building world-class models become the possibility of any data scientist, rather than solely the domain of large companies with large budgets.
2007 - 2012

Bachelor's Degree in Mechanical Engineering

University of Alberta - Edmonton, Alberta, Canada

Libraries/APIs

Scikit-learn, PyTorch, Keras, SpaCy

Tools

GitHub, MATLAB

Languages

Python 3, Python, SQL, Bash, R

Industry Expertise

Project Management

Paradigms

Data Science

Platforms

Amazon Web Services (AWS), Linux, Google Cloud Platform (GCP)

Other

Document Processing, Custom BERT, Artificial Intelligence (AI), Regression, Classification, Machine Learning, Deep Learning, Natural Language Processing (NLP), Feature Engineering, Time Series Analysis, Data Cleaning, Data Visualization, Data Engineering, Statistical Analysis, Generative Pre-trained Transformers (GPT), Real-time Data, Predictive Analytics, Fairseq, Web Scraping, Ensemble Methods, Attribution Modeling, Cloud, Statistics, Sports, Product Management, Mechanical Engineering

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