Go Back
Senior Data Scientist Loc: San Francisco, CA
2017-01-04 23:50:32
Job Type:
Full Time only
Budget $:
100,000 - 200,000
- Be part of an experienced team in Natural Language Understanding, Graph, Deep Learning, and Enterprise Applications with intimate knowledge of the domain
- Help us to introduce mainstream NLU into Enterprise ApplicationsYou would have access to a unique set of data capturing all interactions between businesses, which is sought after by many researchers.
- We are uniquely positioned with significant Intellectual Property
Responsibilities: - Define: Work with customers and internal stakeholders to define hypotheses and models. We are dealing with all aspects of Business-to-Business sales and marketing problems and first to apply data science to them.
Document: - Write clear, concise descriptions of how insights can be converted into repeatable actions.
Test: - Continually iterate your models and refine assumptions, data sources and more.
Communicate: - Drive understanding and buy-in among all stakeholders at all levels.
Requirements: - PhD in Computer Science, Math, Statistics Computational MathematicsOR Masters in related field with
- 2+ years industry experience using NLP/IR/MLStrong background in algorithms and dealing with large-scale data problems
- Experience with Hadoop or Spark or other large-scale data processing platforms
- Experience with SQL or NoSQL databasesProven ability to solve problems using state of the art technology
- Proven ability to innovate when necessary, but not reinvent the wheelIntuition and experience with NLP/IR or graph data is a plus
- Proven ability to apply machine learning to a wide range of problems
Skills:- Imagination beyond what has been done before
- Experimental yet able to create something useful
- Flexibility to deal with ambiguity in requirements
- Hands on, and not afraid to wear multiple hats
- Passion for career growth and development into a leadership position
- Ability to provide technical guidance and leadership to other engineers
Key Skills: