AppZen delivers the world’s leading AI platform for modern finance teams. Starting with business spend, we automate manual process, uncover problems, and optimize decision making for enterprises around the globe, including one-fourth of the Fortune 500. Our platform combines patented deep learning, computer vision, and semantic analysis with intelligence from thousands of online data sources to understand financial transactions in business context and make decisions before those transactions happen. AppZen is a must have for CFOs and their teams to reduce spend, achieve compliance, and streamline process.
We’ve taken off this year! Since we released our platform in 2016, over 1,500 enterprises have standardized on AppZen, including three of the top ten banks, four of the top ten media companies, three of the top ten pharmaceutical manufacturers, two of the top five aerospace companies, and five of the top ten software providers. We were a Gartner Cool Vendor last year, have been recognized as one of the fastest-growing technology companies in the market, and we just announced $50 million in Series C funding.
We are looking for a Data Scientist to come and work on our growing AI stack. If you are excited about Natural Language Processing and Semantic Reasoning, AppZen is the right place for you to use and grow your skills.
- Expert in developing code in Python
- PhD/MS in Computer Science with focus on Natural Language Processing
- Self starter who can be productive from the first day
- 2-3 years work experience in machine learning in industry
- Knowledge and experience with ontologies, taxonomies, semantic meaning representation frameworks (RDF/OWL), linked data.
- Familiarity with database queries and data analysis processes (SQL, Python, Java)
- Information/knowledge extraction from structured/unstructured text (knowledge or statistics based)
- Information retrieval tools such as Elastic Search, Lucene, Solr
- Experience in both R&D environment and product development will be a plus
- Experience in one or more of the following areas: entity/relation extraction, normalization, text summarization, semantic search, word/paragraph/document embedding, ranking, ontology-aware IR
- NLP algorithm implementation experience as well as the ability to modify standard algorithms (e.g. change objectives, work-out the math and implement)
- Experience and/or motivation to work on modern deep learning approaches to NLP: word/paragraph embedding, representation learning, text/sentiment classification, ambiguity disambiguation
- Experience with neural networks and deep learning frameworks (such as Keras, tensorflow, torch)
- Excellent communication skills
- Track-record of having developed novel algorithms, e.g. publications in one or more of the following: KDD, WWW, NIPS, ISWC, NAACL, ACL, SIGIR, EMNLP, ICML etc