Crossboundary
Financial Services
Description
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
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
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