Responsibilities

• Clean, structure, and analyze large datasets (e.g., smart meter data, site-level operational data, revenue data exports)
• Develop Python scripts to automate recurring data cleaning and aggregation workflows
• Conduct time-series analysis to assess site performance (e.g., consumption growth, uptime, load profiles, revenue trends)
• Support cross-site performance comparisons and prototype impact assessments
• Identify anomalies, trends, and patterns in operational data
• Develop visualizations and dashboards to communicate findings clearly
• Support the preparation of data-driven insights for donor reports, internal memos, and sector publications
• Contribute to strengthening the Lab’s internal data infrastructure and analytical tools

Required Technical Skills:

• Python (Intermediate to Advanced)
• Strong experience with Pandas (data cleaning, merging, grouping, aggregations)
• Experience with NumPy (numerical computations and array operations)
• Experience handling and analyzing large CSV datasets
• Experience conducting time-series data analysis (resampling, rolling averages, trend analysis)
• Strong data cleaning skills
• Proficiency in Excel (formulas, structured sheets)
• Basic data visualization skills (Matplotlib / Plotly or similar)

Who you are

• Self-starter who is passionate about creating lasting change in underserved markets
• Takes on new types of work, even without prior experience/direct supervision
• Passionate about using data to improve energy access and climate solutions
• Highly analytical and detail-oriented
• Comfortable working with messy, real-world datasets
• Able to operate independently and manage structured deliverables
• Able to communicate technical findings clearly and succinctly