Brian – Machine learning, Data Science, Big Data

A multi-strategy hedge fund management firm that now focuses on delivering a financial platform for investors.

• Architected hybrid Natural Language Processing (NLP) + LLM pipelines to convert unstructured financial calls, news, and filings into structured, actionable investment signals, enabling clients to identify market-moving events faster and reducing time-to-insight for high-value investment decisions.
• Led fine-tuning of transformer and LLM models (FinBERT, BERT, GPT variants) for financial event extraction, sentiment analysis, and summarization, increasing signal accuracy and cutting manual analyst effort by 40%, directly improving operational efficiency and platform scalability.
• Integrated multi-source financial signals into production pipelines, implementing embeddings and ranking systems to surface high-impact events in real-time, accelerating client decision-making and enhancing platform value for enterprise investment teams.
• Developed advanced embeddings and clustering frameworks to identify recurring financial events and emerging market trends, enabling proactive alerts that empowered clients to capitalize on high-priority investment opportunities ahead of the market.

The world’s leading digital cross-device graph. It enables marketers to identify a brand customer or related household across multiple devices, unlocking critical use cases across programmatic targeting, media measurement, attribution, and personalization globally.

• Spearheaded the migration from a deterministic device matching system to a scalable ML-driven cookieless household clustering pipeline, improving privacy-compliant ad targeting and increasing campaign ROI by 20–30%;
• Built and productionized high-accuracy household and individual clustering models, reducing targeting errors by 25% and improving operational efficiency across ad campaigns;
• Led the Data & AI Platform initiative, designing and deploying core ML platform components across GCP, PyTorch, Vertex AI, and TFX, streamlining experimentation pipelines and accelerating model iteration cycles;
• Acted as a technical multiplier for the team, managing and mentoring engineers on ML productionization, which directly improved deployment reliability and accelerated team velocity;

Data & AI platform solutions for various IBM external clients across diverse industries for ensuring the scalability of their data and machine learning models.

• Spearheaded the design and implementation of distributed ML systems for enterprise clients, translating complex client requirements into scalable solutions that directly improved operational efficiency;
• Deployed scalable real‑time data pipelines on PySpark and Airflow/Kubernetes, processing millions of records daily with automated anomaly detection, improving data reliability and operational visibility for enterprise clients;
• Designed internal Data & AI platform supporting terabyte‑scale distributed pipelines and CI/CD on Kubernetes, boosting developer productivity 2x and reducing infrastructure costs 40%;

The team provided data modeling solutions to various external clients in multiple industries through IBM, addressing their specific business use cases.

• Built ML models (XGBoost, Random Forest) for oil well failure prediction, improving accuracy by 11% via custom metrics and NLP feature engineering.;
• Developed an NLP classifier using TensorFlow, achieving 92% accuracy, and built an Angular dashboard for legal teams as an MVP, securing a business deal with IBM valued at over $1 million.;
• Automated rule-based analytics on DB2, identifying 1,200+ high-risk cases for government audits.;

The revenue service of the Canadian federal government, and most provincial and territorial governments. The CRA collects taxes, administers tax law and policy, and delivers benefit programs and tax credits.

• Built an ML pipeline (Scrapy, Scikit-Learn) to flag non-compliant businesses, improving audit targeting efficiency by 30%, recovering over $1 million dollar back in taxes.;
• Implemented a web scraping solution using ScraPy, BeautifulSoup with features like rotating proxies, dynamic user-agents, and rate limiting to handle anti-scraping mechanisms and ensure reliable data extraction to support auditors.