About SRITNE: 

The ideal candidate would be working for Srini Raju Center for IT and Networked Economy (SRITNE). SRITNE is a multi-disciplinary research center aimed at fostering rigorous and relevant ICT centric research, with focus on ‘management of ICT’ and on the ‘enabling-capabilities of ICT for businesses, governments and society. Researches in these areas are aimed at leading to a better understanding of how ICT creates and can create value for business and society. SRITNE’s efforts in research, education and outreach are aligned with these trends shaping the production and consumption of technology. The center enables interaction between business leaders, students, ISB faculty, and the academic community at large, and provides an important opportunity for these stakeholders to co-create value in a rapidly changing business context. URL: http://www.isb.edu/srini-raju-center-for-it-and-networked-economy provides more details on the center.

Best Match: 

You should consider yourself a good fit for this position if you an experienced, highly motivated data scientist with a track record in machine learning, in particular, Natural Language Processing (NLP) and analysis of unstructured data. The ideal candidate should be able to demonstrate broad knowledge of core NLP tasks, like tagging, syntactic and semantic parsing, named entity recognition, user intent understanding algorithms etc. Implementation experience and programming skills will also be given weight. The position presupposes a graduate degree or equivalent in Natural Language Processing or in Computer Science with a clearly NLP-based thesis.

Skills and Qualification Requirements: 

1) Masters or higher in data mining or machine learning; or equivalently, bachelor’s degree in Computer Science/ Statistics/ Mathematics with at least 2 years’ experience with Machine Learning, NLP, Document Classification, Topic Modelling and Information Extraction with a proven track record of applying them to real problems and real data

2)  Experience working with big data systems, concepts and tools.

3)  Strong team player with cross-functional skills 

4)  Experience with noisy and/or unstructured textual data 

5)  Strong coding proficiency in Python, R and SQL

6)  Knowledge of various text mining algorithms and their use-cases as well as text pre-processing and normalization techniques 

7)  Excellent problem solving skills

8)  Strong verbal and written communication skills 

9)  Exposure to economic problems and econometric techniques desirable

10) Motivation to learn and strong work ethic