ZonForce Technology
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist with a focus on the energy domain, offering a remote contract. Candidates must have strong Python, machine learning, and generative AI skills, along with experience in model development and performance monitoring.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
November 5, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
United States
-
🧠 - Skills detailed
#ML (Machine Learning) #Databricks #Libraries #AI (Artificial Intelligence) #Data Exploration #NumPy #Pandas #Regression #Python #TensorFlow #Data Science #Classification #Clustering #Visualization #ADF (Azure Data Factory) #Deployment #Data Analysis #Matplotlib #PyTorch #Azure Data Factory #Monitoring #Compliance #Azure #Data Manipulation #Hugging Face
Role description
ONLY W2 CANDIDATES FOR THIS OPPORTUNITY.
Title - Data Scientist (Energy Domain Preferred)
Location - Remote
Responsibilities:
Python Proficiency: Strong hands-on experience in Python for data manipulation, analysis, and model development using libraries like Pandas, NumPy, and Scikit-learn.
• Machine Learning Expertise: Advanced knowledge of traditional ML techniques (e.g., regression, classification, clustering) and frameworks like TensorFlow or PyTorch.
• Generative AI: Experience in developing and fine-tuning generative AI models (e.g., GPT, LLMs) using frameworks like OpenAI or Hugging Face.
• Prompt Engineering: Proven ability to design and optimize prompts for generative AI models to enhance AI agent performance.
• Model Development and Evaluation: Expertise in building, testing, and optimizing models with robust evaluation techniques (e.g., F1-score, AUC-ROC, BLEU scores) and hyperparameter tuning.
• AI Agent Development: Hands-on experience in creating AI agents integrating machine learning, reasoning, and heuristics for prioritization and decision-making.
• Data Exploration: Strong skills in exploratory data analysis (EDA) and feature engineering using visualization libraries like Matplotlib and Seaborn.
• Performance Monitoring: Experience in setting up pipelines to monitor model performance and ensure accuracy, efficiency, and ethical compliance.
• Pipeline Development: Proven ability to design, build, and automate end-to-end pipelines for data preparation, model training, evaluation, and deployment using tools like Databricks, Azure Data Factory, or similar orchestration frameworks
ONLY W2 CANDIDATES FOR THIS OPPORTUNITY.
Title - Data Scientist (Energy Domain Preferred)
Location - Remote
Responsibilities:
Python Proficiency: Strong hands-on experience in Python for data manipulation, analysis, and model development using libraries like Pandas, NumPy, and Scikit-learn.
• Machine Learning Expertise: Advanced knowledge of traditional ML techniques (e.g., regression, classification, clustering) and frameworks like TensorFlow or PyTorch.
• Generative AI: Experience in developing and fine-tuning generative AI models (e.g., GPT, LLMs) using frameworks like OpenAI or Hugging Face.
• Prompt Engineering: Proven ability to design and optimize prompts for generative AI models to enhance AI agent performance.
• Model Development and Evaluation: Expertise in building, testing, and optimizing models with robust evaluation techniques (e.g., F1-score, AUC-ROC, BLEU scores) and hyperparameter tuning.
• AI Agent Development: Hands-on experience in creating AI agents integrating machine learning, reasoning, and heuristics for prioritization and decision-making.
• Data Exploration: Strong skills in exploratory data analysis (EDA) and feature engineering using visualization libraries like Matplotlib and Seaborn.
• Performance Monitoring: Experience in setting up pipelines to monitor model performance and ensure accuracy, efficiency, and ethical compliance.
• Pipeline Development: Proven ability to design, build, and automate end-to-end pipelines for data preparation, model training, evaluation, and deployment using tools like Databricks, Azure Data Factory, or similar orchestration frameworks
