Data Analyst - Energy Markets - Remote

Darum lohnt es sich

About the Role Our client is seeking a meticulous and analytical Data Analyst to join their remote team, focusing on the dynamic energy markets.

Key Responsibilities • Collect, process, and clean large, complex datasets from various energy market sources. • Perform statistical analysis to identify trends, patterns, and anomalies in energy data. • Develop and maintain dashboards and reports to visualize key performance indicators and market insights. • Create predictive models for energy prices, demand, and supply fluctuations. • Collaborate with traders, researchers, and business development teams to inform strategy. • Ensure data accuracy, integrity, and security across all analytical processes.

Benefits • Competitive salary and performance-based incentives. • Comprehensive health and wellness benefits. • Fully remote work arrangement with flexible hours. • Professional development opportunities and access to training resources. • The chance to work with complex data in a critical and growing industry – the energy sector.

In this role, you will be responsible for collecting, cleaning, analyzing, and interpreting large datasets related to energy production, consumption, and pricing to provide actionable insights.

You will support strategic decision-making by identifying trends, forecasting market movements, and assessing the impact of various factors on energy commodities.

This is a fantastic opportunity for a data-driven professional to make a significant impact from anywhere, contributing to our client's understanding and strategy in the rapidly evolving global energy landscape.

Requirements • Bachelor's degree in Statistics, Economics, Data Science, or a related quantitative field. • Minimum of 4 years of experience as a Data Analyst, with a focus on the energy sector or financial markets. • Proficiency in data analysis tools and programming languages such as Python, R, or SQL. • Experience with data visualization tools (e.g., Tableau, Power BI). • Strong understanding of statistical modeling, forecasting techniques, and econometrics. • Excellent analytical thinking and problem-solving skills, with strong attention to detail.