The Team: The Data science team is a newly formed applied research team within S&P Global Ratings that will be responsible for building and executing a bold vision around using Machine Learning, Natural Language Processing, Data Science, knowledge engineering, and human computer interfaces for augmenting various business processes.
The Impact: This role will have a significant impact on the success of our data science projects ranging from choosing which projects should be undertaken, to delivering highest quality solution, ultimately enabling our business processes and products with AI and Data Science solutions.
What’s in it for you: This is a high visibility team with an opportunity to make a very meaningful impact on the future direction of the company. You will work with other highly accomplished team members to
Implement data science algorithms
Solve business problems using data science methods
Collaborate effectively with technical and non-technical partners
Create state of the art Augmented Intelligence, Data Science and Machine Learning solutions.
Assist in tracking quantitative and qualitative metrics to measure process and/or content. Provides fact-based interpretation and analysis of findings.
Responsibilities: As an intern you will be responsible for building AI and Data Science models. You will need to rapidly prototype various algorithmic implementations and test their efficacy using appropriate experimental design and hypothesis validation.
S&P Global Ratings
Currently enrolled in PhD or MS in Computer Science, Computational Linguistics, Artificial Intelligence, Statistics, or related field.
Strong ability to code in Python or Java
Experience with Financial data sets, or S&P’s credit ratings process is highly preferred.
Knowledge and working experience in one or more of the following areas: Natural Language Processing, Machine Learning, Question Answering, Text Mining, Information Retrieval, Distributional Semantics, Data Science, Knowledge Engineering
How to apply:
Please mention NLP People as a source when applying