Liam Connell, Developer in Chicago, IL, United States
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Liam Connell

Verified Expert  in Engineering

Software Developer

Location
Chicago, IL, United States
Toptal Member Since
December 1, 2018

Liam has 4 years experience in all things data, including data engineering roles, machine/deep learning projects, and the harnessing of the cloud computing tools. With those three pillars of data, he develops powerful tools that can be applied to either business processes, strategic decision making, or consumer products. Liam prides himself on strong two-way communication, alignment with client goals, and the highest standards.

Portfolio

Boston Consulting Group
Amazon Web Services (AWS), Google Cloud Platform (GCP), Azure, TensorFlow...
Auth0
Amazon Web Services (AWS), Terraform, Apache Airflow, Redshift, Python

Experience

Availability

Part-time

Preferred Environment

Amazon Web Services (AWS), Terraform, Python, Git, OS X

The most amazing...

...thing I've coded is a variational auto-encoder that uses encoding gradients to generate synthetic data and computationally approximate the Wasserstein distance.

Work Experience

Machine Learning Engineer/Consultant

2019 - PRESENT
Boston Consulting Group
  • Developed a scalable machine learning framework in order to deploy an automated analytics product, which was leveraged by other data scientists.
  • Architected ML Pipeline that ingested, processed and modeled over a TB of data daily.
  • Wrote a guide to best practices in developing scalable ML.
Technologies: Amazon Web Services (AWS), Google Cloud Platform (GCP), Azure, TensorFlow, Python

Data Engineer

2016 - PRESENT
Auth0
  • Created an ML-based customer health score that was able to improve sales and customer success personnel allocation by 80%.
  • Designed and developed an over-quota tracking process that drove a campaign to half a million dollars in increased ARR, 10% of the quarter’s ARR gains.
  • Developed and maintained a Redshift data warehouse along the entire pipeline: Coordinating with other engineering teams for designing import processes; ETL design and implementation; Dimensional modeling; Coordinating with the business community to ensure effective use of the data warehouse; designing Machine Learning processes to aid in decision making and gain insights.
Technologies: Amazon Web Services (AWS), Terraform, Apache Airflow, Redshift, Python

A Tour Through TensorFlow with Financial Data

https://liamconnell.github.io/jekyll/update/2016/07/18/a-tour-through-tensorflow-with-financial-data.html
A tutorial that runs through implementations of Deep Learning algorithms as applied to the problem of stock market prediction using TensorFlow. It takes the philosophy of using TensorFlow's lowest level tools in order to build a solid understanding of auto-gradient software and the ML algorithms themselves.

This project has been very popular since it was released, especially since at the time of release (early 2016), TensorFlow was a new technology and tutorials had stayed comfortably in the sphere of toy implementations like MNIST digit recognition. I regularly responded to monthly emails from researchers attempting to replicate the code, many of whom were Ph.D. candidates themselves.

Languages

Python, SQL, R

Libraries/APIs

TensorFlow, NumPy, Scikit-learn, Keras, Pandas, PySpark, Spark ML

Storage

PostgreSQL, Redshift, MongoDB

Paradigms

ETL

Industry Expertise

Teaching

Other

Data Warehouse Design, Machine Learning, Data Warehousing, Online Tutoring, Deep Learning, Artificial Intelligence (AI), Generative Adversarial Networks (GANs), Variational Autoencoders, Image Recognition, AWS Cloud Architecture, Natural Language Processing (NLP), Predictive Modeling, Financial Modeling, Reinforcement Learning, Deep Reinforcement Learning, GPT, Generative Pre-trained Transformers (GPT)

Frameworks

Django, Django REST Framework

Tools

Git, Terraform, Apache Airflow

Platforms

OS X, Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP)

2011 - 2015

Bachelor's Degree in Mathematics

Colby College - Waterville, Maine

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