Analytica is hiring a
Senior Data Scientist

Summary

The job is for a remote Senior Data Scientist- NLP position at Analytica, a fast-growing consulting company. The role involves applying statistical programming, modeling, visualization techniques, data mining, and forecasting skills to analyze public sector problems in financial regulatory or health projects.

Requirements

• Master's degree required, and PhD preferred in Statistics, Mathematics, Computer Science, or similar
• High degree of experience utilizing SAS, R, or Python to support NLP use cases such as Document Summarization, Named Entity Recognition, Sentiment Analysis, and/or Topic Modeling
• At least four years of experience developing scalable, production-ready NLP solutions using sci-kit learn, Keras, TensorFlow, PyTorch, Spark NLP
• Experience using Microsoft Azure translation services or Azure AI
• Experience leveraging transformer architecture to develop NLP models
• Experience with open source NLP packages such as Gensim, SpaCy, or NLTK
• Experience with BERT, GPT-J, RoBERTa, T5 or other transformers
• Experience working in a cloud environment
• Experience coordinating and maintaining user stories
• Must be a US citizen
• Must be able to obtain and maintain a Public trust security clearance

Responsibilities

• Apply statistical programming, modeling, visualization techniques, data mining, and forecasting skills to analyze challenging public sector problems
• Experience with machine translation and transcription of foreign language documents using Microsoft Azure translation services
• Pre-processing - Demonstrate the skills and experience to collect, clean, and prepare data sets for input into a computational model using technologies such as Python, SAS, or R
• Feature Engineering and Attribute Evaluation - Candidate must demonstrate experience with NLP feature engineering methods such as TF-IDF, word2vec, GloVe, and FastText identifying the key determinants for modeling that exist in the business process and within existing data sets as well as selecting evaluation protocols (model techniques)
• Modeling - Candidates will have practiced skills and experience selecting modeling techniques to fit the business problem. Examples will include techniques such as machine learning (ML) supervised and unsupervised learning, regression, neural networks and deep learning, natural language processing, etc
• Validation - Strong candidates will describe their experience with investigating, reporting, and justifying model results
• Visualization- Experience in presenting the results of their modeling activities, depicting the insights realized, and explaining the relevance of their results to the organization’s business challenges

Benefits

• Competitive compensation with opportunities for bonuses
• Employer-paid health care
• Training and development funds
• 401k match