Senior AI/ML Data Scientist

Overview

On Site
$140,000+
Contract - W2
Contract - Independent
100% Travel

Skills

Python
NLP
AI

Job Details

Job Title: Senior AI/ML Data Scientist Duration: Long term
Location: Woodlawn, MD (Onsite 5 days a week)
Note: Selected candidate must be willing to work on-site in Woodlawn, MD 5 days a week.
Key Required Skills:
Strong knowledge of AI/ML/LLM, Python, NLP, Generative AI and experience in the clinical domain.
Position Description:

  • Stay updated on the new methods in NLP, ML and Generative AI
  • Understand real world challenges and develop automated data solutions
  • Develop, test, and deploy new techniques for NLP understanding
  • Scalable development/deployment of ML and Generative AI approaches (such as Large Language Models (LLMs)
  • Train and optimize NLP/LLM models and create Python based pipelines
  • Determine the nature of analytic problems, evaluate options, and offer recommendations for resolution.
  • Advise on the methods and data needed and/or available to evaluate the (intelligence or data) problem.
  • Collaborate with data collectors and analysts to identify and close gaps on complex monitoring problems.
  • Provide accurate, timely, complex, and sophisticated data analysis.

Basic Qualifications:

  • Bachelor's degree in Statistics, Applied Mathematics, Computer Science, or Information Science with industry experience on NLP, data science, AI/ML/LLM engineering.
  • Minimum 8 Year (s) of Data Scientist experience
  • Must be able to obtain and maintain a Public Trust.

Required Skills:

  • Experience with Natural Language Processing (NLP), Generative AI and Large Language Models (LLM)
  • Fluency in Python Programming, version control and collaboration with GIT, standard Python packages (ex. Pandas, numpy, matplotlib) and ML frameworks
  • Knowledge of TensorFlow, PyTorch, Pandas, scikit-learn, NLTK, Azure ML (optional), Amazon Web Services EC2.
  • Experience with scalable data engineering frameworks such as Apache Spark and orchestration frameworks such as Airflow, and/or experience with semantic search.
  • Expert knowledge in conducting data analysis and applying advanced statistical concepts and ML methods to build, train, test, and evaluate a variety of supervised and unsupervised analytic models.
  • Experience with ML model deployment and operations like DevOps, MLOps, LLMOps.
  • Experience with NLP and Generative AI libraries like regular expressions (e.g., spacy, langchain), text annotation tools and semantic frameworks.
  • Ability to clean and process large amounts of real-world data.
  • Experience retrieving and manipulating data from a variety of data sources included DB2, Oracle, SQL Server, Hadoop and flat files.
  • Experience with database management systems (e.g., PostgresSQL, MySQL, SQLite, SQL, etc.)
  • Excellent analytical skills to identify potential risks and propose effective solutions.
  • Excellent problem-solving skills, ability to collaborate with cross-functional teams and proven communication in written and verbal formats to various audiences to include executive leadership.

Desired Skills:

  • Prior experience working on applications in the clinical domain.
  • Prior experience with federal or state governments IT projects.
  • Experience with, or the ability and willingness to learn distributed processing via the Hadoop ecosystem, i.e., Spark, Impala and Hive.
  • Experience working in an analytical research environment.
  • Experience in parallel processing such as GPU programming with CUDA
  • Experience with Mathematica
  • Experience using markup languages such as LaTeX, HTML, etc.
  • Experience with Natural Language Processing for anomaly detection

Education:

  • Bachelor's degree with 12+ years of experience
  • Must be able to obtain and maintain a Public Trust
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.