Monitor asset performance using SCADA/DCS, historian, weather, production and maintenance data.
Analyse availability, PR/PRstc, DC/string availability, alarms and fault codes at plant, subsystem and component level.
Work with large datasets to identify trends, patterns and anomalies.
Interrogate and validate data quality (e.g. gaps, anomalies, comms losses, configuration changes).
Design, refine and document performance metrics, thresholds and losses.
Apply M&V approaches to quantify the impact of events, operational changes and projects on energy and revenue.
Maintain visibility of key KPIs such as revenue losses, soiling, data integrity, DC availability and forecasting accuracy.
Manage structured data requests from the asset platform to Engineering and site teams related to plant performance and losses.
Deliver performance insights using historical trend analysis.
Analyse alarm frequency and recurring equipment failures and link these to maintenance activities and reliability.
Apply statistical and data-science techniques to understand energy profiles, drivers and losses.
Develop analytical workflows and algorithms in Python or R.
Monitor, develop and improve performance dashboards and monitoring screens.
Support and conduct root-cause-analysis (RCA) investigations.
Participate in cross-functional projects and maintenance activities.

Degree in Engineering (Electrical / Mechanical / Industrial), Data Science, Statistics, Mathematics, Physics or a closely related technical field.
1–3 years’ experience in an asset-performance, energy-analytics, industrial-analytics or data-science role working with operational time-series data.
Hands-on experience analysing SCADA/DCS, historian or IoT / time-series data in a production or operations environment.
Demonstrated proficiency in SQL for data extraction, joining and aggregation from relational databases / data warehouses.
Practical experience in Python or R for data wrangling, statistical analysis, visualisation and development of custom analytical models or algorithms.
Experience building and maintaining performance reports or dashboards (e.g. Power BI, MatplotLib or similar).
Practical application of statistical and/or data-science techniques to real-world datasets.
Experience working with cross-functional teams.

Minimum requirements:

Degree in Engineering (Electrical / Mechanical / Industrial), Data Science, Statistics, Mathematics, Physics or a closely related technical field.
1–3 years’ experience in an asset-performance, energy-analytics, industrial-analytics or data-science role working with operational time-series data (not purely financial or commercial analytics).
Hands-on experience analysing SCADA/DCS, historian or IoT / time-series data in a production or operations environment.
Demonstrated proficiency in SQL for data extraction, joining and aggregation from relational databases / data warehouses.
Practical experience in Python or R for data wrangling, statistical analysis, visualisation and development of custom analytical models or algorithms.
Experience building and maintaining performance reports or dashboards (e.g. Power BI, MatplotLib or similar) used by operational stakeholders.
Practical application of statistical and/or data-science techniques to real-world datasets (e.g. regression, time-series modelling, anomaly detection, segmentation, forecasting).
Experience working with cross-functional teams (operations, engineering, maintenance, data/IT) and turning analytical findings into concrete actions or projects.

Advantageous:

Familiarity with renewable-energy assets (solar PV, wind and/or storage) and their key performance KPIs (availability, PR, yield, curtailment, grid events, alarms).
Direct experience in solar PV, wind or battery energy-storage performance analysis with site-level or fleet-level responsibility.
Exposure to advanced analytics / ML for operational use cases (e.g. automated anomaly detection, fault prediction, production forecasting, condition monitoring).
Experience with time-series historians (e.g. AVEVA PI, TGS Prediktor or similar industrial data platforms).
Familiarity with CMMS/ERP systems (e.g. IFS) and integrating maintenance / asset-management data into performance analytics.
Experience working in or closely with a Remote Operations Centre / control room environment.
Knowledge of energy measurement and verification (M&V) standards or methodologies and their application to performance analytics.

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