Federal – Natural Language Processing (NLP) Technical Lead

Job Overview

Organization: Accenture Federal ServicesLocation: Woodlawn, MDAccenture Federal Services, a wholly owned subsidiary of Accenture LLP, is a U.S. company with offices in Arlington, Virginia. Accenture’s federal business has served every cabinet-level department and 30 of the largest federal organizations. Accenture Federal Services transforms bold ideas into breakthrough outcomes for clients at defense, intelligence, public safety, civilian and military health organizations.We believe that great outcomes are everything. It’s what drives us to turn bold ideas into breakthrough solutions. By combining digital technologies with what works across the world’s leading businesses, we use agile approaches to help clients solve their toughest problems fast-the first time. So, you can deliver what matters most.This is an extraordinary opportunity to build a rewarding career – with excellent benefits – at Accenture Federal Services. Working in highly collaborative teams for world-leading clients, we’ll nurture your talent in an inclusive culture that values diversity. While fast-tracking your career, you’ll have the flexibility to pursue your specialist passions. So, whatever your work and life goals, we’ll help you achieve them. Sooner.NLP Technical Lead will provide technical leadership and engineering support for a Task Order using advanced natural language processing techniques, deep learning and big data analytics.THE WORK….* NLP Technical Lead will lead inter-disciplinary teams, develop, and productionize Machine Learning/Deep Learning models* Define reference and solution architectures that support cloud initiatives, big data and data lake use cases, and traditional data platforms* Create and maintain conceptual, logical, and scalable physical data models that embrace Domain Driven Design best practices* As the NLP Technical Lead, you will have experience with and ability to formulate solutions using one or more of the following models: Deep Learning (CNNs, RCNNs, LSTMs), GANs, Autoencoders, Siamese Networks, Logistic Regression, Linear Regression, Support Vector Machines, Hidden Markov Models, Conditional Random Fields, Latent Dirichlet Allocation