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Senior Data Scientist – Advanced Machine Learning & AI

This role is for a Senior Data Scientist with a Master’s degree in Machine Learning or related field, focusing on advanced ML/AI, mentoring, and utilizing GCP tools. Contract length and pay rate are unspecified. Industry experience in ML frameworks is required.
🌎 - Country
United States
💱 - Currency
$ USD
💰 - Day rate
Unknown
Unknown
🗓️ - Date discovered
February 14, 2025
🕒 - Project duration
Unknown
🏝️ - Location type
Unknown
📄 - Contract type
Unknown
🔒 - Security clearance
Unknown
📍 - Location detailed
Dallas, TX
🧠 - Skills detailed
#Data Science #Neural Networks #Unsupervised Learning #Supervised Learning #Clustering #Leadership #AI (Artificial Intelligence) #Statistics #TensorFlow #BigQuery #Monitoring #GCP (Google Cloud Platform) #Scala #Data Engineering #Computer Science #PyTorch #Model Evaluation #Dataflow #ML (Machine Learning) #Deployment #Datasets
Role description
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A technology services client of ours is looking for Senior Data Scientist skills their ongoing projects.

Below are the additional details of this role:

Required Skills:
• Master’s degree in Machine Learning, Computer Science, Statistics, or a related field
• Mentor junior team members and drive thought leadership in ML/AI practices.
• Analyze large, unstructured datasets, applying techniques like feature selection, dimensionality reduction, and clustering.
• Research and integrate state-of-the-art ML frameworks (e.g., TensorFlow, PyTorch) and algorithms.
• Apply MLOps practices to ensure robust monitoring, retraining, and model lifecycle management.
• Use GCP tools like Vertex AI, BigQuery, and Dataflow for model training, experimentation, and deployment.
• Solid understanding of data preprocessing, feature engineering, and model evaluation techniques.
• Proficiency in advanced ML techniques, including ensemble methods, neural networks, and unsupervised learning.
• Collaborate with data engineers and product teams to translate business objectives into impactful AI solutions.
• Design end-to-end ML pipelines, optimizing for performance, scalability, and reliability.
• Innovate in areas such as explainable AI, transfer learning, and real-time decision-making systems.