Senior Data Scientist - GenAI

Summary

Join Analytica as a remote Senior Data Scientist- GenAI to contribute to long-term federal client engagements in financial regulatory or health projects within the DC Metro area. This role involves applying statistical programming, modeling, visualization, data mining, and forecasting skills to analyze complex public sector challenges. Analytica, recognized among the fastest-growing businesses in the US, collaborates with US government clients across various missions. The company offers competitive compensation, including bonuses, employer-paid healthcare, training funds, and 401k matching. Responsibilities include data preprocessing, feature engineering, model selection and validation, and results visualization. A Master's degree is required, and a PhD is preferred, along with extensive experience in NLP and GenAI.

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
• Experience with multi-modal GenAI/LLMs and prompt engineering techniques
• At least four years of experience developing scalable, production-ready NLP solutions using sci-kit learn, Keras, TensorFlow, PyTorch, Spark NLP
• 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

• 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. Strong candidates will explain various methods you have applied using common pre-processing functions such as stop word removal, stemming, lemmatization, and tokenization
• 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

• Opportunities for bonuses
• Employer-paid health care
• Training and development funds
• 401k match