Associate Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Associate Data Scientist, offering a remote contract position with a focus on Python and deep learning solutions. Key skills include expertise in TensorFlow, PyTorch, Docker, and Kubernetes, with a strong emphasis on applied machine learning experience.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 24, 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
#Code Reviews #Programming #Monitoring #TensorFlow #Deep Learning #Project Management #Docker #Libraries #Python #ML (Machine Learning) #PyTorch #Quality Assurance #Data Science #Data Quality #Datasets #Scala #Kubernetes #Deployment #Data Integrity
Role description
About the Role
This position requires strong expertise in developing and implementing deep learning solutions, with an emphasis on Python programming as the primary development language. The selected candidate will play a key role in a highly collaborative, virtual team of technical specialists, actively contributing to every stage of the software development lifecycle—from early research and prototyping to production deployment and ongoing optimization.
This is a dynamic role that not only demands technical proficiency but also the ability to work effectively in a distributed, fast-paced environment. The successful applicant will have the opportunity to solve challenging problems, build innovative machine learning solutions, and directly influence the performance and reliability of deployed systems.
Key Responsibilities
1. Design and Development: Create, refine, and optimize machine learning and deep learning features in close collaboration with other data scientists, engineers, and subject matter experts.
1. Project Ownership: Oversee the full lifecycle of data science projects, including research, experimentation, implementation, testing, deployment, and post-deployment monitoring.
1. Data Preparation: Perform preprocessing and cleaning of both structured and unstructured data, ensuring datasets are ready for analysis and model training.
1. Data Quality Assurance: Maintain high standards of data integrity, validating datasets and ensuring consistency for analytical use.
1. Performance Monitoring: Define, implement, and track performance metrics for deployed models and software solutions to ensure reliability and scalability.
1. Collaboration: Participate in regular team discussions, knowledge sharing, and code reviews to ensure best practices are followed and project goals are achieved.
Desired Skills and Experience
1. Python Expertise: Advanced knowledge of Python programming, with proven experience using scientific and deep learning frameworks such as TensorFlow, PyTorch, or similar libraries for training, tuning, and deploying models.
1. Containerization: Hands-on experience working with containerization technologies such as Docker and orchestration tools like Kubernetes.
1. Applied ML/DL Knowledge: Strong understanding of how to apply machine learning and deep learning methods to solve practical, real-world problems using complex datasets.
1. Autonomous Work Style: Ability to work independently, manage multiple tasks, and adapt quickly to evolving project requirements in a distributed team setting.
1. Remote Collaboration: Comfortable working remotely with proficiency in digital collaboration tools, including video conferencing, screen sharing, and project management platforms.
About the Role
This position requires strong expertise in developing and implementing deep learning solutions, with an emphasis on Python programming as the primary development language. The selected candidate will play a key role in a highly collaborative, virtual team of technical specialists, actively contributing to every stage of the software development lifecycle—from early research and prototyping to production deployment and ongoing optimization.
This is a dynamic role that not only demands technical proficiency but also the ability to work effectively in a distributed, fast-paced environment. The successful applicant will have the opportunity to solve challenging problems, build innovative machine learning solutions, and directly influence the performance and reliability of deployed systems.
Key Responsibilities
1. Design and Development: Create, refine, and optimize machine learning and deep learning features in close collaboration with other data scientists, engineers, and subject matter experts.
1. Project Ownership: Oversee the full lifecycle of data science projects, including research, experimentation, implementation, testing, deployment, and post-deployment monitoring.
1. Data Preparation: Perform preprocessing and cleaning of both structured and unstructured data, ensuring datasets are ready for analysis and model training.
1. Data Quality Assurance: Maintain high standards of data integrity, validating datasets and ensuring consistency for analytical use.
1. Performance Monitoring: Define, implement, and track performance metrics for deployed models and software solutions to ensure reliability and scalability.
1. Collaboration: Participate in regular team discussions, knowledge sharing, and code reviews to ensure best practices are followed and project goals are achieved.
Desired Skills and Experience
1. Python Expertise: Advanced knowledge of Python programming, with proven experience using scientific and deep learning frameworks such as TensorFlow, PyTorch, or similar libraries for training, tuning, and deploying models.
1. Containerization: Hands-on experience working with containerization technologies such as Docker and orchestration tools like Kubernetes.
1. Applied ML/DL Knowledge: Strong understanding of how to apply machine learning and deep learning methods to solve practical, real-world problems using complex datasets.
1. Autonomous Work Style: Ability to work independently, manage multiple tasks, and adapt quickly to evolving project requirements in a distributed team setting.
1. Remote Collaboration: Comfortable working remotely with proficiency in digital collaboration tools, including video conferencing, screen sharing, and project management platforms.
