Data Scientist

Solid Experience with Natural Language Processing (NLP), Python, NLP frameworks, SQL, Pandas, NLTK and SPACy.

Experience with Generative AI and Large Language Models (LLM)

Excellent Communication skills

Hands on experience in Python, NLP frameworks, SQL, Pandas, NLTK, SPACy and LLMs

Well versed in SQL and analyzing trends and transactional data.

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

Experience building cloud native solutions on AWS

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.

Bachelor’s degree in Statistics, Applied Mathematics, Computer Science, or Information Science with industry experience on Python, NLP frameworks, SQL, Pandas, NLTK and SPACy, data science, and AI/ML/LLM engineering.

Overall 10+ years’ experience in IT industry

Solid Experience with Natural Language Processing (NLP), Python, NLP frameworks, SQL, Pandas, NLTK and SPACy.

Experience with Generative AI and Large Language Models (LLM)

Evidence of true self-starter and operating independently.

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.

Excellent Communication skills.

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.

Prior experience with federal or state governments IT projects.

Industry experience preferred

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.