Brian McMahon

Data Scientist

https://www.opentalent.in/brian-mcmahon

Building Nous Ergon, a suite of agentic tools, including Crucible, an experiment harness for financial systems.

Seattle, WA

Key Strengths

Demonstrated experience with various data science projects, including recommender systems, predictive modeling, and AI/ML applications.
Proficiency in Python and Jupyter Notebook, essential tools for data scientists.
Exposure to advanced machine learning concepts like Bidirectional LSTMs and LightGBM.
Experience with MLOps-like practices through projects like 'crucible-executor' and 'crucible-predictor'.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are exclusively personal and academic, indicating a strong interest in self-driven learning and exploration within data science. However, the lack of team-based or professional experience makes it difficult to assess cultural fit in a collaborative work environment. The projects are diverse within the data science domain, but there's no indication of broader organizational or business acumen.

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a focus on technical execution.

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Projects

morning-signal

April 13, 2026 – Present

Open-source, self-hostable daily briefing podcast — Claude with web search writes it, Polly or Google Chirp3 TTS voices it, and it publishes an RSS feed you subscribe to in any podcast app. Runs on your own Anthropic + AWS/GCP keys, no platform lock-in. pip install morning-signal.

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crucible-predictor

March 10, 2026 – Present

Nous Ergon — LightGBM predictor: 21-day market-relative alpha forecasts, walk-forward validation, veto gate

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crucible-dashboard

March 9, 2026 – Present

Nous Ergon — read-only Streamlit dashboard: portfolio performance, signal quality, research theses, GBM predictions

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crucible-executor

March 2, 2026 – Present

Nous Ergon — a harness for rigorous AI/ML experiments in finance: multi-agent research, ML prediction, risk-gated execution, and a self-tuning backtester on AWS

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recommender_system

June 9, 2018 – August 7, 2018

A music recommender system based on data from last.fm.

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quadcop2

April 25, 2018 – May 5, 2018

Teaching a quadcopter drone how to fly.

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street_team_optimize

April 7, 2018 – April 7, 2018

Analyze NYC MTA subway and demographic data to optimize street team deployment.

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bitcoin_prediction

April 7, 2018 – November 3, 2018

Predict the price of bitcoin through linear regression.

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AI_Jukebox

April 3, 2018 – April 8, 2018

Music generator utilizing a Bidirectional LSTM architecture in Keras.

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McMahon_Metis

January 16, 2018 – April 7, 2018

Work compiled during Metis' Data Science Bootcamp, Winter 2018.

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Key Strengths

Demonstrated experience with various data science projects, including recommender systems, predictive modeling, and AI/ML applications.
Proficiency in Python and Jupyter Notebook, essential tools for data scientists.
Exposure to advanced machine learning concepts like Bidirectional LSTMs and LightGBM.
Experience with MLOps-like practices through projects like 'crucible-executor' and 'crucible-predictor'.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a focus on technical execution.

Brian McMahon

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit