Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer on a contract basis, focusing on optimizing predictive models using CatBoost and Python. Key skills include expertise in scikit-learn, data preprocessing, and model validation. Location and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 11, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Unknown
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Philadelphia, PA
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🧠 - Skills detailed
#ML (Machine Learning) #Pandas #Python #GIT #AI (Artificial Intelligence) #Datasets #Jupyter #NumPy #Data Analysis #Model Validation #Libraries #Data Engineering #Version Control
Role description
Responsibilities:
• Own, maintain, and optimize an existing production-grade predictive model built using CatBoost and standard Python ML libraries.
• Develop robust data preprocessing pipelines and feature engineering strategies to improve model accuracy using large, historical datasets from job tracking applications.
• Implement rigorous model validation techniques, track performance metrics (e.g., RMSE, MAE), and proactively identify areas for improvement.
• Collaborate directly with Data Engineers, Software Developers, and Product Managers to integrate and serve ML models within production systems.
• Analyze model output, investigate anomalies, and ensure overall model reliability and reproducibility.
• Clearly document your work and communicate complex model behavior and findings to both technical and non-technical stakeholders.
• Explore and prototype advanced modeling techniques (e.g., Bayesian models, statistical methods) for future product enhancements.
Must-Have Skills:
• Expert-level proficiency in Python for machine learning and data analysis, with proven professional experience.
• Deep, hands-on expertise with scikit-learn, pandas, NumPy, and standard data preprocessing libraries.
• Practical, professional experience with CatBoost (or similar gradient boosting frameworks like XGBoost or LightGBM), including model training, validation, and hyperparameter tuning.
• Solid understanding of end-to-end ML lifecycle: feature engineering, model training, validation, hyperparameter tuning, and performance measurement.
• Proven ability to clean, analyze, and derive insights from large, structured, real-world datasets.
• Professional experience with Jupyter Notebooks and Git for version control.
• Excellent problem-solving skills and the ability to communicate complex concepts clearly and effectively.
#MachineLearning #ML #MachineLearningEngineer #AI #Python #CatBoost #ScikitLearn #XGBoost #DataScience #MLOps #TechJobs #Hiring #Comcast #ConstructionTech #PredictiveAnalytics #LinkedInJobs
Responsibilities:
• Own, maintain, and optimize an existing production-grade predictive model built using CatBoost and standard Python ML libraries.
• Develop robust data preprocessing pipelines and feature engineering strategies to improve model accuracy using large, historical datasets from job tracking applications.
• Implement rigorous model validation techniques, track performance metrics (e.g., RMSE, MAE), and proactively identify areas for improvement.
• Collaborate directly with Data Engineers, Software Developers, and Product Managers to integrate and serve ML models within production systems.
• Analyze model output, investigate anomalies, and ensure overall model reliability and reproducibility.
• Clearly document your work and communicate complex model behavior and findings to both technical and non-technical stakeholders.
• Explore and prototype advanced modeling techniques (e.g., Bayesian models, statistical methods) for future product enhancements.
Must-Have Skills:
• Expert-level proficiency in Python for machine learning and data analysis, with proven professional experience.
• Deep, hands-on expertise with scikit-learn, pandas, NumPy, and standard data preprocessing libraries.
• Practical, professional experience with CatBoost (or similar gradient boosting frameworks like XGBoost or LightGBM), including model training, validation, and hyperparameter tuning.
• Solid understanding of end-to-end ML lifecycle: feature engineering, model training, validation, hyperparameter tuning, and performance measurement.
• Proven ability to clean, analyze, and derive insights from large, structured, real-world datasets.
• Professional experience with Jupyter Notebooks and Git for version control.
• Excellent problem-solving skills and the ability to communicate complex concepts clearly and effectively.
#MachineLearning #ML #MachineLearningEngineer #AI #Python #CatBoost #ScikitLearn #XGBoost #DataScience #MLOps #TechJobs #Hiring #Comcast #ConstructionTech #PredictiveAnalytics #LinkedInJobs
