Senior Data Scientist

Overview

We are seeking an experienced NLP Data Scientist / Machine Learning Engineer to provide advanced data science and Natural Language Processing (NLP) support for a data-driven business analytics organization.

The successful candidate will leverage Python, SQL, NLP, machine learning, deep learning, and advanced data analytics to transform large volumes of structured and unstructured data into actionable insights that support senior-level decision-making related to production, resources, personnel, and organizational performance.

What will you do?

• Conduct sophisticated analysis of structured and unstructured data using Natural Language Processing (NLP) techniques.
• Develop and implement NLP solutions using Python libraries such as:
  • spaCy
  • Gensim
  • NLTK
• Select appropriate NLP libraries, preprocessing techniques, modeling approaches, and evaluation methodologies based on the analytical problem.
• Develop text classification and topic modeling solutions using Python.
• Build machine learning models using Scikit-learn and other machine learning frameworks.
• Develop deep learning solutions using technologies such as:
  • PyTorch
  • TensorFlow
  • Keras
• Utilize the Hugging Face Transformers library and model hub for NLP applications.
• Apply encoder-decoder and generative language models to NLP use cases.
• Evaluate model performance and develop practical approaches for measuring effectiveness.
• Leverage GPUs and accelerated computing to train and execute machine learning and deep learning workloads.
• Provide NLP subject matter expertise supporting organizational initiatives.
• Analyze and preprocess large volumes of raw structured and unstructured data, including text-based datasets.
• Clean, normalize, and prepare data for analytical and machine learning applications.
• Design and implement advanced Extract, Transform, and Load (ETL) processes.
• Conduct advanced statistical analysis across personnel, intelligence, production, and performance metrics.
• Assist in selecting and developing appropriate research methodologies.
• Develop practical approaches for measuring organizational and program performance

Do you have what it takes?

• Active TS/SCI with Polygraph
• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical discipline preferred.
• Demonstrated professional or academic experience performing Natural Language Processing (NLP).
• Strong programming experience with Python.
• Experience selecting and utilizing Python NLP libraries such as spaCy, Gensim, and/or NLTK.
• Experience with deep learning frameworks such as:
  • PyTorch
  • TensorFlow
  • Keras
• Experience with Hugging Face Transformers and associated models.
• Experience developing machine learning models for:
  • Text classification
  • Topic modeling
  • Other NLP applications
• Experience using Scikit-learn and/or deep learning models.
• Experience working with encoder-decoder and generative language models.
• Experience preprocessing and analyzing structured and unstructured datasets.
• Strong proficiency with SQL.
• Experience developing advanced SQL queries using CTEs, set operations, aggregate functions, and nested subqueries.
• Experience developing complex ETL processes and data transformations.
• Experience communicating analytical methodologies, model decisions, and results.
• Experience utilizing version control and development tools such as GitHub and Jenkins.
• Experience leveraging GPUs for accelerated computing.
• Strong analytical and statistical problem-solving capabilities.
• Ability to translate complex analytical findings into information that is understandable to customers and senior leadership.
• Active TS/SCI with Polygraph
• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical discipline preferred.
• Demonstrated professional or academic experience performing Natural Language Processing (NLP).
• Strong programming experience with Python.
• Experience selecting and utilizing Python NLP libraries such as spaCy, Gensim, and/or NLTK.
• Experience with deep learning frameworks such as:
  • PyTorch
  • TensorFlow
  • Keras
• Experience with Hugging Face Transformers and associated models.
• Experience developing machine learning models for:
  • Text classification
  • Topic modeling
  • Other NLP applications
• Experience using Scikit-learn and/or deep learning models.
• Experience working with encoder-decoder and generative language models.
• Experience preprocessing and analyzing structured and unstructured datasets.
• Strong proficiency with SQL.
• Experience developing advanced SQL queries using CTEs, set operations, aggregate functions, and nested subqueries.
• Experience developing complex ETL processes and data transformations.
• Experience communicating analytical methodologies, model decisions, and results.
• Experience utilizing version control and development tools such as GitHub and Jenkins.
• Experience leveraging GPUs for accelerated computing.
• Strong analytical and statistical problem-solving capabilities.
• Ability to translate complex analytical findings into information that is understandable to customers and senior leadership.
• Conduct sophisticated analysis of structured and unstructured data using Natural Language Processing (NLP) techniques.
• Develop and implement NLP solutions using Python libraries such as:
  • spaCy
  • Gensim
  • NLTK
• Select appropriate NLP libraries, preprocessing techniques, modeling approaches, and evaluation methodologies based on the analytical problem.
• Develop text classification and topic modeling solutions using Python.
• Build machine learning models using Scikit-learn and other machine learning frameworks.
• Develop deep learning solutions using technologies such as:
  • PyTorch
  • TensorFlow
  • Keras
• Utilize the Hugging Face Transformers library and model hub for NLP applications.
• Apply encoder-decoder and generative language models to NLP use cases.
• Evaluate model performance and develop practical approaches for measuring effectiveness.
• Leverage GPUs and accelerated computing to train and execute machine learning and deep learning workloads.
• Provide NLP subject matter expertise supporting organizational initiatives.
• Analyze and preprocess large volumes of raw structured and unstructured data, including text-based datasets.
• Clean, normalize, and prepare data for analytical and machine learning applications.
• Design and implement advanced Extract, Transform, and Load (ETL) processes.
• Conduct advanced statistical analysis across personnel, intelligence, production, and performance metrics.
• Assist in selecting and developing appropriate research methodologies.
• Develop practical approaches for measuring organizational and program performance