Machine Learning Engineer Resume

SUMMARY

Technical lead, Data Scientist, and Machine Learning (ML) engineer on multiple successful production ML/AI projects.
Core skills: software development (10 years), ML (5 years), Natural Language Processing/NLP (5 years), tech leading (3 years).
Published AI/ML researcher.
Technical skills include Python, R, SQL/NoSQL databases, distributed data stores (“Big Data”), and public cloud.
Flexible communicator (written and spoken) comfortable with technical and non - technical audiences.
Experience in finance, energy, retail, legal services, manufacturing, and healthcare industries.
Significant international and cross-cultural experience.

PROFESSIONAL EXPERIENCE

Confidential

Machine Learning Engineer

Responsibilities:

Build custom production ML systems for Confidential Cloud customers; advise customers (including C-level) on AI/ML strategy.
Built search tools to help US Patent and Trademark Office patent examiners ensure the validity of newly issued patents.
Built an ML system that makes daily interest rate estimates for $4T of financial assets for a major bank. Led a team of six. After the project, customer spend grew +489% over six months, and the customer is becoming a Confidential Cloud public reference.
Architected an ML-based pricing system for an e-commerce platform with $3B+ of annual purchases. Planned the $1M+ project to deploy the system. Expected additional profit from the system is $10M-$20M annually. Led one engineer.
Designed a Kubeflow/Kubernetes-based machine learning training and serving platform for a Fortune 100 manufacturer.
Built an NLP system to extract facts (e.g., chemicals, dosage) from unstructured paper pharmaceutical documents into structured (SQL) data for a healthcare company with $100B+ in annual revenue.
Wrote, with collaborators, two technical blog posts (12) on Confidential Cloud products. The posts have over 15,000 views.
Manage global AI/ML feature requests from Confidential Cloud customers, aid Product Managers with feature prioritization.
Trained over 1000 individuals (Googlers and public) in topics including NLP, Kubeflow, sequence models, and ML in production.
Co-wrote and maintain Confidential -wide legal guidance on safe and compliant use of public data.

Confidential

Senior Data Scientist

Responsibilities:

Built a (production) NLP pipeline that included structured prediction, unsupervised learning, and graph processing.
Conducted literature searches and implemented ML models from research articles, making changes to fit our use cases.
Designed and supervised a $250,000 data labeling project.
Won an internal technical award f or training the first deep learning model in my department.

Confidential

Senior Consultant

Responsibilities:

Wrote multiple sections of SunGard’s SEC Rule 613 bid and a whitepaper on our transaction processing system.
Developed a n ML model using SEC EDGAR financial filing data to predict future corporate bankruptcies.
Designed, built, and deployed a set of analytics and visualizations (Tableau/MySQL) for the CEO of an energy investment fund.
Received 2015 Confidential STAR award (awarded to 4 out of 200 consultants).