Bespoke Technologies, Inc.
BT-167 – Data Scientist
Location: Chantilly (fully on-site, no remote option)
MUST HAVE A POLY CLEARANCE TO APPLY. Those who do not have a Poly clearance will not be considered.
Bespoke Technologies is seeking a
Data Scientist
for a position in Chantilly, VA.
Required Skills & Experience:
Python, PyTorch, Flask (knowledge at minimum, ability to quickly pick up).
Familiarity with REST APIs.
Statistics background/experience.
Basic understanding of NLP.
Demonstrated experience in Python, JavaScript, and R.
Demonstrated experience employing machine learning and deep learning modules such as Pandas, Scikit, TensorFlow, PyTorch.
Demonstrated experience with statistical inference, as well as building and understanding predictive models, using machine learning methods.
Demonstrated experience with large-scale text analytics.
Desired Skills & Experience:
Demonstrated hands‑on experience performing research or development with natural language processing and working with, deploying, and testing Convolutional Neural Networks (CNN), large‑language models (LLMs) or foundational models.
Demonstrated experience developing and deploying testing and verification methodologies to evaluate algorithm performance and identify strategies for improvement or optimization.
Demonstrated experience deploying machine learning models on multimedia data, to include joint text, audio, video, hardware, and peripherals.
Demonstrated experience with Linux System Administration and associated scripting languages (Bash).
Demonstrated experience with Android configuration, software development, and interfacing.
Demonstrated experience in embedded systems (Raspberry Pi).
Other Skills & Experience:
Develops and conducts independent testing and evaluation methods on research‑grade algorithms in applicable fields.
Reports results and provides documentation and guidance on working with the research‑grade algorithms.
Evaluates, integrates and leverages internally‑hosted data science tools.
Customizes research‑grade algorithms to be optimized for memory and computational efficiency through quantizing, trimming layers, or through custom methods.
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MUST HAVE A POLY CLEARANCE TO APPLY. Those who do not have a Poly clearance will not be considered.
Bespoke Technologies is seeking a
Data Scientist
for a position in Chantilly, VA.
Required Skills & Experience:
Python, PyTorch, Flask (knowledge at minimum, ability to quickly pick up).
Familiarity with REST APIs.
Statistics background/experience.
Basic understanding of NLP.
Demonstrated experience in Python, JavaScript, and R.
Demonstrated experience employing machine learning and deep learning modules such as Pandas, Scikit, TensorFlow, PyTorch.
Demonstrated experience with statistical inference, as well as building and understanding predictive models, using machine learning methods.
Demonstrated experience with large-scale text analytics.
Desired Skills & Experience:
Demonstrated hands‑on experience performing research or development with natural language processing and working with, deploying, and testing Convolutional Neural Networks (CNN), large‑language models (LLMs) or foundational models.
Demonstrated experience developing and deploying testing and verification methodologies to evaluate algorithm performance and identify strategies for improvement or optimization.
Demonstrated experience deploying machine learning models on multimedia data, to include joint text, audio, video, hardware, and peripherals.
Demonstrated experience with Linux System Administration and associated scripting languages (Bash).
Demonstrated experience with Android configuration, software development, and interfacing.
Demonstrated experience in embedded systems (Raspberry Pi).
Other Skills & Experience:
Develops and conducts independent testing and evaluation methods on research‑grade algorithms in applicable fields.
Reports results and provides documentation and guidance on working with the research‑grade algorithms.
Evaluates, integrates and leverages internally‑hosted data science tools.
Customizes research‑grade algorithms to be optimized for memory and computational efficiency through quantizing, trimming layers, or through custom methods.
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