Senior Machine Learning Engineer Resume

SUMMARY

AI/Machine Learning & NLP Engineer, Entrepreneur, and Technology Leader. Expertise in state - of-the-art AI fields, including Deep Learning, NLP and Predictive Analytics.
Extensive experience with Finance, FinTech, and enterprise-scale Conversational Agent and NLP applications.
Seeking new challenges in senior research, engineering, and leadership roles.

PROFESSIONAL EXPERIENCE

Senior Machine Learning Engineer

Confidential

Responsibilities:

Led major Machine Learning and NLP initiatives within Confidential .
Led strategic R&D projects to build cutting-edge ML, predictive analytics, and NLP solutions across securities trading, regulatory surveillance, fraud detection, risk analytics, and operations automation.
Engineered and led implementation of NLP solutions for automated analysis of unstructured text documents, including contracts, regulatory docs, and financial research.
Developed Machine Learning solutions for automated analysis of unstructured and alternative data sources (including real-time news) for trading algorithms and post-trade operations.
Developed generic conversational agent architecture using machine learning to streamline building chatbots as interfaces to internal structured and unstructured data sources.
Led predictive analytics projects around client segmentation and recommender applications.
Led multiple teams of of 3-12 researches and developers.
Technologies: Python, TensorFlow, PyTorch, Keras, Spark, scikit-learn, NLTK, spaCy, Hadoop, ELK, NoSQL, GCP, AWS

Lead Machine Learning Engineer / Data Scientist

Confidential

Responsibilities:

Led/developed Machine Learning and NLP components of next-generation IVR - interactive customer response system.
Analyzed extremely complex multi-modal data set containing tens of millions of records.
Built prototype RNN (LSTM) driven conversational agent to handle multiple bank businesses.
Built Boosted Decision Tree model to predict customer intent and mobile interaction preferences.
Led team of 8 technologists.
Technologies: Python, TensorFlow, Spark, scikit-learn, Core NLP, NLTK, Word2Vec, NoSQL

Machine Learning & NLP Engineer and Consultant

Confidential

Responsibilities:

Provide outside consulting services to early stage companies developing products and platforms leveraging advanced AI technologies.
Domains include predictive advertising and recommendation, deep online content analysis, and FinTech.
Engineered cutting-edge Machine Learning and NLP technologies to drive core business.
Deep analysis of multimodal behavioral data to generate more personalized and relevant product and business recommendation models.
Deep models of users and online content for predictive marketing.
Trading signals from alternative data sources.
Research in automated conversation agents (chatbots) leveraging advanced technologies including Word2Vec, large scale captured data, Ensemble Methods, and Deep Learning.
Technologies: Python, Spark, TensorFlow, scikit-learn, NLTK, spaCy, Word2Vec, ELK, NoSQL, AWS

Quantitative Researcher / Portfolio Manager

Confidential

Responsibilities:

Researched and traded systematic financial strategies around real-time news and other market-moving events.
Extensive use of full-stack NLP to analyze unstructured text and alternative data sources to identify actionable signals.
Extensive use of Machine Learning techniques to characterize market reaction and generate profitable trades.
Research and development of Deep Learning methods for high-frequency market analysis.
Deep Natural Language processing and analysis: full NLP stack, Word2Vec, LDA, semantic models, pragmatics, discourse analysis.
Extensive use of advanced ML technologies: Neural Nets, Ensemble Methods, Kernel Methods.
Technologies: Python, scikit-learn, TensorFlow, Keras, NLTK, Core NLP, Word2Vec, C++, SQL

Quantitative Researcher / Portfolio Manager

Confidential

Responsibilities:

Researched and traded high-frequency financial strategies around real-time news and other market-moving events.
Developed highly accurate predictive ML models for multiple time-frame market reaction to economic events.
Extracted actionable signals by mining unstructured textual sources such as Twitter feeds, streaming news, blogs, and full-length news articles using advanced full-stack NLP.
Architected and led implementation of innovative trading platform for economic events.
Extensive use of Neural Nets, Support Vector Machines, Kernel Methods.
Technologies: Python, scikit-learn, NLTK, Stanford Core NLP, C++, SQL