Senior NLP Data Scientist & ML Engineer

Vosper Thornycroft Group

McLean (VA)

Hybrid

USD 150,000 - 230,000

Full time

8 days ago

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Job summary

Vosper Thornycroft Group seeks an experienced NLP Data Scientist / ML Engineer to provide advanced data science support for a data-driven analytics organization. You will transform large volumes of structured and unstructured data into actionable insights to support senior decision-making related to production, resources, personnel, and performance.

The role requires Python NLP expertise, DL/ML experience with PyTorch, TensorFlow, and Keras, and extensive SQL.

Qualifications

Active TS/SCI with Polygraph.
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical discipline.
Experience performing NLP.
Strong Python programming.
Experience with spaCy, Gensim, and/or NLTK.
Experience with PyTorch, TensorFlow, and Keras.
Experience with Hugging Face Transformers and related models.
Experience developing ML models for text classification, topic modeling, or NLP applications.
Experience with Scikit-learn and/or DL models.
Experience using encoder-decoder/generative language models.
Experience preprocessing/analyzing structured and unstructured data.
Strong proficiency with SQL.
Experience writing advanced SQL queries (CTEs, set ops, aggregates, nested queries).
Experience developing complex ETL processes.
Experience communicating methodologies, model decisions, and results.
Experience using GitHub and Jenkins.
Experience leveraging GPUs for accelerated computing.
Strong analytical and statistical problem-solving abilities.
Ability to translate complex findings to customers and senior leadership.

Responsibilities

Conduct sophisticated analysis of structured and unstructured data using NLP techniques.
Develop NLP solutions using Python libraries (spaCy, Gensim, NLTK).
Select NLP libraries, preprocessing, modeling approaches and evaluation methods.
Develop text classification and topic modeling solutions using Python.
Build ML models with Scikit-learn and other frameworks.
Develop deep learning solutions using PyTorch, TensorFlow, and Keras.
Utilize Hugging Face Transformers for NLP applications.
Apply encoder-decoder and generative language models to NLP use cases.
Evaluate model performance and measure effectiveness.
Leverage GPUs for accelerated computing in training and inference.
Provide NLP SME support for organizational initiatives.
Analyze and preprocess large volumes of raw data, including text datasets.
Clean, normalize, and prepare data for analytics and ML.
Design and implement advanced ETL processes.
Analyze stats across personnel, production, and performance metrics.
Develop research methodologies and communicate results to stakeholders.