Manager-Data Scientist

Position Overview We are seeking a skilled Data Scientist with a focus on Natural Language Processing (NLP) to join our team. The ideal candidate brings 3+ years of hands‑on experience working with text data, building NLP models, and developing production‑ready machine learning solutions. This role will focus on analyzing unstructured data, designing and implementing NLP models, and collaborating with engineering teams to deploy scalable solutions.

Please note:

United States citizenship is a requirement for this position. This position requires a Public Trust security eligibility determination post‑hire.

• Conduct detailed exploratory data analysis (EDA) on structured and unstructured text datasets to derive insights and inform model development.
• Design, build, and evaluate models for a variety of NLP tasks, including:
  • Text classification
  • Named entity recognition (NER)
  • Information extraction from unstructured documents
• Develop and refine regular expressions, traditional NLP pipelines, and transformer‑based models to support business use cases.
• Write high‑quality, production‑grade Python code, following best practices for scalability, testing, and maintainability.
• Apply Generative AI techniques and prompt engineering to enhance automation and downstream applications.
• Collaborate closely with machine learning engineering teams to deploy, monitor, and optimize NLP solutions in production.
• Utilize cloud technologies such as Azure or AWS to build, train, and manage ML workloads.

• United States citizenship (required).
• 3+ years of experience in data science, with significant focus on NLP.
• Demonstrated expertise in text‑based EDA, NLP model development, and working with unstructured data.
• Strong proficiency in Python and NLP/ML libraries (e.g., spaCy, NLTK, Hugging Face Transformers, scikit‑learn).
• Hands‑on experience with Generative AI models and prompt engineering.
• Strong understanding of machine learning fundamentals, model evaluation, and experiment design.
• Ability to translate business needs into technical solutions and communicate complex concepts effectively.

• Experience deploying NLP solutions in production environments in partnership with ML engineering teams.
• Hands‑on experience with cloud platforms, including Microsoft Azure or Amazon Web Services (AWS).
• Familiarity with CI/CD workflows, containerization (e.g., Docker), or distributed computing frameworks.