Data Science Practitioner (must be w2, no third party)

We are hiring a Data Science Practitioner for a 100% onsite role in Woodlawn, MD. The selected resource must have a clearable background and be eligible to work on W2. No third party candidates accepted.

Role Description:
Masters +2 years of experience or 6 years relevant work experience
Excellent oral and written communication skills
Formulate and rapidly prototype various approaches as well as effectively communicate the pros and cons of each.
Excellent time management
Ability to contribute to a high-performing, motivated workgroup by applying interpersonal and collaboration skills to achieve project goals
Provide technical guidance in the fields of NLP, Machine Learning, Statistical Methods
Provide data-driven approaches to tackle various business and NLP problems
Ability to contribute to the creation of an environment that motivates individuals to work collaboratively as a team

Requires proficiency in:
Python (required)
Regular Expressions
SQL (PostgreSQL)
No-SQL (MongoDB)
Version control systems (Git)
Experience with ML frameworks: Tensorflow, PyTorch, Transfomers, Scikit-learn XGBoost, LSTM, Keras, Pandas, BERT, CNN, RNN, SVMs, k-Nearest Neighbors, Linear/Logistic Regression and Classification, Ensemble Methods, Graphical Models, Clustering, Tesseract
Information Extraction
Statistical model building (particularly classification)
Ability to draw insights from sparsely labeled textual data.
Ability to leverage domain knowledge as well as ontologies to improve model performance

Strong understanding of statistical modeling and experimental design including:
when to value precision vs recall
bias/variance tradeoffs
handling sampling issues
ability to improve performance on noisy data (both textual and numeric)

Knowledge of and experience using various NLP approaches, particularly:
Pattern recognition/feature extraction
Supervised, Unsupervised, and Semi-Supervised learning techniques
Understanding of various language models (N-Gram, Skipgram, NLM, etc.)
Practical experience leveraging open source libraries for emerging DNN approaches to NLP (transformers, BERT, RoBERTA, etc.)
Chunking/Tokenization
Semantic parsing

The following skills are not required but are highly desired:
Experience with NLP technologies
Experience with machine learning
Web Service technologies such as SOAP, WSDL, WS-Security, MTOM, SWA
Relational Databases such as DB2, Oracle, MySQL, SQL, JDBC
NoSQL databases such as MongoDB and HBase
Hadoop, Spark, HDFS, MapReduce, YARN, Scala, MapReduce, Pyspark
XML processing experience such as XSD, XPath, XSL, XSLT, etc.
ebXML
IBM MQ Series

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