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 leadership role with an opportunity to make a very meaningful impact on the future direction of the company. You will work with senior leaders in the organization to define, build, and transform our business.
Responsibilities: As an Associate Director you will be responsible for building AI and Data Science solution design, reference implementations, and algorithmic implementations. You will need to work closely with internal stakeholders and users, mentor junior scientists, and identify opportunities that lead to business impact and ultimately drive the Data Science vision.
Basic Qualifications: MS in Computer Science, Computational Linguistics, Artificial Intelligence or related field with 7+ years of relevant industry experience
- PhD in Computer Science, Computational Linguistics, Artificial Intelligence or related field with 4+ years of relevant industry experience
- Experience in mentoring or managing junior scientists and engineers, working with business stakeholders and users, providing research direction and solution design
- 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
- Proficient programming skills in a high-level language (e.g. Java, Scala, Python, C/C++, Perl, Matlab, R)
- Experience with statistical data analysis, experimental design, and hypotheses validation
- Project-based experience with some of the following tools:
- Applied machine learning (e.g. libSVM, Shogun, Scikit-learn or similar)
- Natural Language Processing (e.g., ClearTK, ScalaNLP/Breeze, ClearNLP, OpenNLP, NLTK, or similar)
- Statistical data analysis and experimental design (e.g., using R, Matlab, iPython, etc.)
- Information retrieval and search engines, e.g. Solr/Lucene
- Distributed computing platforms, such as Hadoop (Hive, HBase, Pig), Spark, GraphLab
- Databases (traditional and noSQL)