GlacierGrid is hiring a
Data Scientist

Summary

Join GlacierGrid as a Data Scientist to contribute to innovative and sustainable solutions in the energy industry.

Requirements

• Master’s degree (or completion of Master’s degree requirements) in Computer Science, Business Analytics, or a related field [OR: Bachelor’s degree in Computer Science, Business Analytics, or related field and 2 years of experience in data science, business analytics, or a related field]
• Understanding of data visualization
• Background in Predictive Analytics
• Knowledge of Statistical Data Analysis
• Understanding of Machine Learning algorithms
• Completion of at least 1 hands-on project (done through university coursework, internships or similar) which utilizes Python programming language
• Understanding of data processing
• Knowledge of Data Analytics
• Background in Business Analytics
• Knowledge of Natural Language Processing
• Completion of at least 1 hands-on project (done through university coursework, internships or similar) which utilizes Data Modelization
• Knowledge of Lean-Method startups

Responsibilities

• Collect and acquire data from various sources
• Clean and preprocess raw data to ensure its quality and suitability for analysis
• Conduct exploratory data analysis to understand the characteristics and patterns in the data
• Apply statistical techniques to analyze data and draw meaningful insights
• Develop and implement machine learning models to solve specific business problems
• Assess the performance of machine learning models using metrics such as accuracy, precision, recall, and F1 score
• Optimize models for better results
• Create new features or variables from existing data to improve the performance of machine learning models
• Create visualizations to communicate findings and insights to both technical and non-technical stakeholders
• Clearly communicate the results of data analyses to non-technical stakeholders and collaborate with cross-functional teams to implement data-driven solutions
• Build models for predictive analytics, such as forecasting future trends, customer behavior, or business outcomes
• Design and analyze A/B tests to evaluate the impact of changes or interventions in a business process