AI/ML Data Scientist (Databricks)
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI/ML Data Scientist (Databricks) on a contract basis, offering a competitive pay rate. Key skills include Databricks architecture, model evaluation metrics, RAG implementation, deep learning, and MLOps practices. Remote work is available.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 20, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Remote
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#AI (Artificial Intelligence) #ML (Machine Learning) #Predictive Modeling #Data Engineering #Deployment #Model Deployment #Databricks #Anomaly Detection #"ETL (Extract #Transform #Load)" #Deep Learning #Data Science #Monitoring #Classification #RNN (Recurrent Neural Networks) #Model Evaluation
Role description
One of my clients is looking for an AI/ML Data Scientist (Databricks) - USA (Remote) for a contract role.
Required Skills & Qualifications:
• Strong knowledge of Databricks architecture for data engineering and machine learning workflows.
• Solid understanding of model evaluation metrics such as precision, recall, and F1-score.
• Hands-on experience with RAG (Retrieval-Augmented Generation) implementation.
• In-depth knowledge of deep learning architectures, including CNN, RNN, and Transformer, with the ability to explain key differences.
• Practical experience applying AI/ML use cases such as classification, anomaly detection, and predictive modeling.
• Expertise in MLOps practices, including model deployment, monitoring, and fine-tuning for performance optimization.
If interested, please share your updated resume.
One of my clients is looking for an AI/ML Data Scientist (Databricks) - USA (Remote) for a contract role.
Required Skills & Qualifications:
• Strong knowledge of Databricks architecture for data engineering and machine learning workflows.
• Solid understanding of model evaluation metrics such as precision, recall, and F1-score.
• Hands-on experience with RAG (Retrieval-Augmented Generation) implementation.
• In-depth knowledge of deep learning architectures, including CNN, RNN, and Transformer, with the ability to explain key differences.
• Practical experience applying AI/ML use cases such as classification, anomaly detection, and predictive modeling.
• Expertise in MLOps practices, including model deployment, monitoring, and fine-tuning for performance optimization.
If interested, please share your updated resume.
