NextGenPros Inc
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist on a remote contract, requiring expertise in cloud platforms (GCP, AWS), Python, and machine learning frameworks. Candidates should have experience with large datasets, time-series forecasting, and deploying models using Docker and Kubernetes.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
July 24, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Data Science #Airflow #ML (Machine Learning) #GCP (Google Cloud Platform) #AWS (Amazon Web Services) #Docker #Python #PyTorch #Cloud #Regression #Forecasting #AI (Artificial Intelligence) #SQL (Structured Query Language) #TensorFlow #Time Series #Datasets #Kubernetes
Role description
Job Title : DataScientist
Location : Remote.
Contract
Technical Skills
• Relevant experience with cloud platforms (e.g., GCP, AWS) and productionizing models (e.g., Cloud Run, Kubernetes, Airflow, Vertex AI, or comparable cloud services).
• Experience working with large-scale and noisy business datasets.
• Strong foundation in Python, time-series forecasting, and frameworks like PyTorch, TensorFlow, or XGBoost.
• Proven skill in containerization (Docker) and deploying services via Google Cloud Run or Kubernetes (GKE/EKS).
• Strong statistical foundation, including experience with time series analysis, regression, and probabilistic modeling.
• Proficiency in Python, along with SQL for data querying and manipulation.
• Have experience in designing, development, and improve machine learning and statistical models for financial forecasting and related applications.
• Experience in Owning the end-to-end modeling lifecycle.
Job Title : DataScientist
Location : Remote.
Contract
Technical Skills
• Relevant experience with cloud platforms (e.g., GCP, AWS) and productionizing models (e.g., Cloud Run, Kubernetes, Airflow, Vertex AI, or comparable cloud services).
• Experience working with large-scale and noisy business datasets.
• Strong foundation in Python, time-series forecasting, and frameworks like PyTorch, TensorFlow, or XGBoost.
• Proven skill in containerization (Docker) and deploying services via Google Cloud Run or Kubernetes (GKE/EKS).
• Strong statistical foundation, including experience with time series analysis, regression, and probabilistic modeling.
• Proficiency in Python, along with SQL for data querying and manipulation.
• Have experience in designing, development, and improve machine learning and statistical models for financial forecasting and related applications.
• Experience in Owning the end-to-end modeling lifecycle.
