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Moderate competition: popular Data Scientist title and mid-level (1–3 years) experience attract many qualified applicants.
Medium sensitivity: core data skills transferable, but solar operational and SCADA domain knowledge is preferred.
Medium strictness due to explicit 1–3 years requirement and mandatory Python, SQL, PySpark, Databricks skills.
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Perform advanced analytics and develop predictive models on large-scale operational time-series datasets from utility-scale solar plants.
Manage and automate data workflows and dashboards using Python, SQL, PySpark, Power BI, and cloud platforms like Azure and Databricks.
Coordinate and track multiple operational analytics projects and Availability Guarantee programs with cross-functional teams, managing deliverables and stakeholder communications.
Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or related quantitative fields.
1–3 years of professional experience in Data Science, Analytics, Data Engineering, Operational Analytics, or Technical Program Management.
Proficiency in Python, SQL, PySpark; experience with large-scale time-series data and data visualization tools (e.g., Power BI).
Strong understanding of statistics and analytical methodologies; excellent communication and project coordination skills.
Experienced in operational analytics within renewable energy or large-scale technical systems involving time-series data.
Able to independently manage multiple technical projects with strong ownership and accountability under ambiguous conditions.
Skilled in cross-functional collaboration and technical communication to diverse stakeholder groups, supporting both customer-facing and internal analytics initiatives.