Data Scientist
3 Openings (Data Engineer, Data Scientist, Data Analyst)
Location: Hybrid (2-3 days a week onsite), Denver CO
Pay Rate: $70 - $80 /hr. on W2 with benefits
Contract: Long Term Contract (possibility of conversion to an FTE)
Summary
The Data Science team is a high-performing group charged with developing analytics solutions for product owners across the company. As part of this group, you will play a pivotal role in supporting a brand new "closed loop" initiative, which involves leveraging NLP and LLM within AI to enhance an outage management tool. Responsibilities include developing and deploying analytical models to detect network outages,processing real- time data, and implementing monitoring and testing for production jobs. Additionally, this role will serve as a liaison between the Data Science and the MLOps team to implement machine learning pipelines and ensure seamless integration.
Responsibilities
• Develop and maintain machine learning models for network outage detection and classification.
• Build models using NLP, LLM, classification, and regression techniques.
• Work on feature engineering and model optimization.
• Analyze and interpret model outputs to provide actionable business insights.
• Collaborate with data engineers to operationalize models in cloud environments.
• Support business intelligence by meeting with stakeholders and refining models.
• Conduct data exploration, feature selection, and high-level ML approaches.
Required Qualifications
• Proficiency in Python or R for modeling and analysis.
• Experience with ML libraries: Scikit-learn, Pandas, XGBoost, Jupyter Notebooks/Labs.
• Strong understanding of classification and regression models.
• Ability to ask the right questions and refine models based on real-world data.
• Comfortable with high-level data analytics and feature importance interpretation.
Preferred Qualifications
• Knowledge of cloud ML services (AWS SageMaker, GCP AI Platform).
• Experience with NLP and LLM for AI-driven analytics.
• Understanding of model deployment and MLOps practices.
3 Openings (Data Engineer, Data Scientist, Data Analyst)
Location: Hybrid (2-3 days a week onsite), Denver CO
Pay Rate: $70 - $80 /hr. on W2 with benefits
Contract: Long Term Contract (possibility of conversion to an FTE)
Summary
The Data Science team is a high-performing group charged with developing analytics solutions for product owners across the company. As part of this group, you will play a pivotal role in supporting a brand new "closed loop" initiative, which involves leveraging NLP and LLM within AI to enhance an outage management tool. Responsibilities include developing and deploying analytical models to detect network outages,processing real- time data, and implementing monitoring and testing for production jobs. Additionally, this role will serve as a liaison between the Data Science and the MLOps team to implement machine learning pipelines and ensure seamless integration.
Responsibilities
• Develop and maintain machine learning models for network outage detection and classification.
• Build models using NLP, LLM, classification, and regression techniques.
• Work on feature engineering and model optimization.
• Analyze and interpret model outputs to provide actionable business insights.
• Collaborate with data engineers to operationalize models in cloud environments.
• Support business intelligence by meeting with stakeholders and refining models.
• Conduct data exploration, feature selection, and high-level ML approaches.
Required Qualifications
• Proficiency in Python or R for modeling and analysis.
• Experience with ML libraries: Scikit-learn, Pandas, XGBoost, Jupyter Notebooks/Labs.
• Strong understanding of classification and regression models.
• Ability to ask the right questions and refine models based on real-world data.
• Comfortable with high-level data analytics and feature importance interpretation.
Preferred Qualifications
• Knowledge of cloud ML services (AWS SageMaker, GCP AI Platform).
• Experience with NLP and LLM for AI-driven analytics.
• Understanding of model deployment and MLOps practices.
