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il y a 1 semaine
FactSet is seeking a Machine Learning Engineer with significant experience in natural language processing and experience working with large volumes of data in a stream or batch processing environment.
This role is located within the Content Engineering AI team and reports to the VP, Director of AI. The team count 3 Machine Learning Engineers and 2 Data scientist.
The primary responsibility for this position will be the development and maintenance of systems for processing unstructured financial data, with a focus on machine learning and natural language text analysis.
Produces production code using machine learning and natural language processing techniques to speed up collection efforts, boost data quality, and enable/improve filtering and search of TVL/FactSet data/content for clients.
Experience with time-series analysis is helpful but not required.
**Requirements**:
- Python data science stack and Jupyter Notebooks (Pandas, Scikit learn, Numpy, etc.)
- Development of Machine Learning or NLP programs
- Experience with NLP libraries (e.g. NLTK, Spacy)
- Development of classifiers
- Development of linear models or classifiers for reporting and analytics => E.g. Regression, Bayesian classifiers, SVM
- Experience working with large volumes of data in a stream or batch processing environment.
- Prior experience working with unstructured data (text content, JSON records) => Feature engineering experience from unstructured data
- Working with Agile development practices
Desired Skills and experiences
- Experience or exposure with AWS environment (SageMaker, S3, Athena, Glue, EC2, ECS)
- Prior experience with Docker and API development
- Familiarity with deep learning libraries (Keras, PyTorch, Tensorflow) => Experience with Tensorboard or visualization and interpretation of deep learning models
- Usage of MongoDB
- Familiarity with big data tool chain (e.g. Pyspark, Hive)
- Experience with R and the RStudio/Shiny tool chain
experiences Recruitment process:
1) Call with HR : 30 minutes
2) Call with the hiring manager : 30 minutes
3) Call with a senior team member - ML topic (Hacker Rank) : 45 minutes
4) Call with a product team member : 30 minutes
