Accenture 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.
We’re seeking a Science Director in the area of Natural Language Processing who wants to apply their skills to work at the leading edge of automation in public sector industries such as customer care, healthcare, social media, and legal and compliance solutions, and want to make their innovations real. We work on problems which are at the intersection of natural language processing, machine learning, and graph analytics. We apply deep learning, transfer learning, graph modelling, and computational linguistic techniques to novel domains. We are looking for skilled, dedicated people who believe their contributions can make the world more productive and a better place to live.
The Natural Language Processing Science Director will work with other data scientists to explore, innovate and advance the fields of artificial intelligence, machine learning, deep learning, and natural language processing across the multiple public sector clients that Accenture Federal serves. The Natural Language Processing Science Director will be part of innovative projects where natural language processing analytics will lead to increased efficiencies in agencies served by Accenture Federal Services. They will apply machine learning and other techniques to design and prototype intelligent, real-world applications that intelligently extract understanding from text and/or audio sources. They will keep abreast of the latest developments in the field by participating in conferences and other venues and can champion promising, new methods to explore. They will proactively identify new areas of research and in partnership with domain teams develop value-based proposals for investment.
What You’ll Do
Design and develop clinical NLP methods that ingest large unstructured data sets, separate signal from noise, and provide insights that directly improve our analytics platform
Interrogate analytical results to resolve algorithmic success, robustness and validity
Automate complex decision models and build data pipelines for ML and NLP leveraging unstructured voice and text data and interpreting model results for end users
A passion to teach and train other data scientists to expand and develop a center of excellence
Analyze and model structured and unstructured data using advanced statistical methods and create structured data from unstructured conversational voice
Ability to perform one or more of the following core domain skills: machine learning (ML), natural language (NLP), texting mining, and statistical methods such as classification, association rules, sentiment, topic modeling, time-series, statistical inference, and validation methods
Ability to communicate complex models and methods to non-technical staff and business leads
Work with a range of structured and unstructured data sources
Build, Improve and extend NLP capabilities.
Research and evaluate new/different approaches to NLP problems.
Produce deliverable results and take them from development to production in collaboration with our engineers.
Accenture Federal Services
MS with 3 years experience or Ph.D. in Computer Science, Computation Linguistics or a related field
Experience with a variety of NLP methods for information extraction, topic modeling, parsing, and relationship extraction
Experience with knowledge databases and language ontologies
Reproducible research methods
Self-driven and works well in an interdisciplinary team with minimal direction
Experience with communicating insights and presenting concepts to a diverse audience
Proficiency in R and/or Python for both quantitative analysis and data engineering, and at least one open-source NLP toolkit such as OpenNLP, CoreNLP, gensim, NLTK, etc.
Expertise in at least 3 of the following: Sentiment Analysis, Entity Extraction, Document Classification, Topic Modeling, Natural Language Understanding (NLU) and Natural Language Generation (NLG)
Strong understanding of text pre-processing and normalization techniques, such as tokenization, POS tagging and parsing and how they work at a low level
Expertise in producing, processing, evaluating and utilizing training data
Good understanding of linguistics and language as a phenomenon
Strong interest in, and knowledge of Artificial Intelligence and its subfields.
Experience with Deep Learning and Word Embeddings
Experience with open-source NLP toolkits such as CoreNLP, OpenNLP, NLTK, gensim, LingPipe, Mallet, etc.
Experience with noisy and/or unstructured textual data (e.g. tweets)
Published work in academic conferences/journals or industry circles
Experience with one or more of Natural Language Processing, Artificial Intelligence, Machine Learning or Deep Learning through research projects, internships, and/or thesis research
Must be an out-of-the-box problem solver, capable of proposing novel solutions to problems, performing experiments to show feasibility of their solutions and working to refine the solutions into a real-world context
Level of experience (years):
Mid Career (2+ years of experience)