Biztec Global
Senior Data Scientist - GenAI
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Scientist - GenAI, requiring 7 years of GenAI experience, proficiency in Python and Git, and expertise in deep learning, generative algorithms, and ethical AI evaluation. Contract length and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
December 25, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
San Francisco Bay Area
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🧠 - Skills detailed
#AI (Artificial Intelligence) #Supervised Learning #Data Science #Deployment #"ETL (Extract #Transform #Load)" #Python #Deep Learning #RNN (Recurrent Neural Networks) #Unsupervised Learning #GIT #Version Control #Programming
Role description
Requirements
• 7 years of experience working as a GenAI Data Science.
• Experience with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git
• Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.)
• Experience with Bedrock, JumpStart, HuggingFace
• Experience evaluating ethical implications of AI and controlling for them (e.g., red-teaming)
• Expertise in supervised learning and unsupervised learning along with experience in deep learning and transfer learning
• Experience in generative algorithms (e.g., GAN, VAE, etc.) as well as pre-trained models (e.g., LLaMa, SAM, etc.)
• Experience developing models from inception to deployment
Requirements
• 7 years of experience working as a GenAI Data Science.
• Experience with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git
• Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.)
• Experience with Bedrock, JumpStart, HuggingFace
• Experience evaluating ethical implications of AI and controlling for them (e.g., red-teaming)
• Expertise in supervised learning and unsupervised learning along with experience in deep learning and transfer learning
• Experience in generative algorithms (e.g., GAN, VAE, etc.) as well as pre-trained models (e.g., LLaMa, SAM, etc.)
• Experience developing models from inception to deployment
