Liam Connell, Developer in New York, NY, United States
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Liam Connell

Verified Expert  in Engineering

Bio

Liam has nine years of experience as a machine learning engineer and engineering leader. He has worked for the past five years as a lead ML engineer in the AI/ML practice area of BCG, where he applies the latest AI and ML techniques to build products across industries and use cases. Drawing on this deep technical experience and exceptional soft skills from his time at an elite consulting firm, Liam brings delivery certainty and impeccable quality to his project execution.

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

Full-time

Preferred Environment

Amazon Web Services (AWS), Terraform, Python, PyTorch, OpenAI API, Apache Airflow

The most amazing...

...thing I've built is an automated insurance assessment platform that used computer vision and NLP to revolutionize the assessments business process.

Work Experience

Lead AI Engineer | Project Lead

2019 - PRESENT
Boston Consulting Group
  • Led the development of an automated assessments platform for a global insurance company using computer vision and aerial image processing.
  • Developed a RAG-based GenAI application for agriculture intelligence, answering questions using around 100,000 documents from the USDA, WASDE, and SEC and transcripts from earnings calls.
  • Leveraged computer vision on satellite imagery and other ML techniques to forecast US corn and soy yield.
  • Developed a GenAI-powered network incident intelligence system for large telecommunications company.
  • Led technical strategy and design at Lighthouse, BCG's technical asset platform, including advanced data warehouse design and operations.
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

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.
2011 - 2015

Bachelor's Degree in Mathematics

Colby College - Waterville, Maine

Libraries/APIs

TensorFlow, NumPy, Scikit-learn, Keras, PyTorch, OpenAI API, Pandas, PySpark, Spark ML

Tools

Git, Terraform, Apache Airflow

Languages

Python, SQL, R

Platforms

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

Storage

PostgreSQL, Redshift, MongoDB

Paradigms

ETL

Industry Expertise

Teaching

Frameworks

Django, Django REST Framework

Other

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

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