Data Scientist - Energy Analytics

The Role 

We are looking for a Data Scientist to develop analytical models and intelligence for energy applications. 

Working closely with engineering and domain experts, you will understand how electrical assets behave, what their measurements represent and how engineering knowledge can be translated into analytical models, calculations, rules and indicators. 

You will work with complex sensor, time-series, telemetry and operational data, developing analytical methods that turn measurements into meaningful information about asset condition, performance and behaviour. 

We are open to considering candidates at both mid-level and senior level, with the position and responsibilities reflecting the successful candidate's experience. 

Technology Environment 

Our data and analytics environment includes Python, SQL, Apache Spark, PySpark, Databricks and MLflow, alongside other cloud-based data technologies. 

Experience with these platforms and technologies is advantageous. Strong underlying programming, analytical and problem-solving skills are more important than familiarity with any specific technology. 

What Makes This Role Different 

This is not a traditional BI or reporting role, nor is it purely focused on developing machine learning models. 

The key challenge is understanding what energy asset data represents, combining measurements with engineering knowledge, and translating that understanding into reliable analytical intelligence. 

You will work on real engineering problems and help develop the analytical capabilities behind solutions used to monitor and understand critical energy infrastructure.

Key Responsibilities 

• Work with engineering and domain experts to understand electrical assets, their components, relationships, operating conditions and behaviour. 

• Translate engineering knowledge into analytical models, calculations, rules, indicators and derived measurements. 

• Analyse sensor, time-series, telemetry and operational data from electrical infrastructure and energy networks. 

• Develop analytical solutions using Python and SQL, including data transformations, feature engineering and calculation logic. 

• Develop methods to assess asset condition, performance and behaviour, identifying anomalies and meaningful changes in operating patterns. 

• Apply statistical methods, anomaly detection, forecasting and machine learning where they provide meaningful value. 

• Validate analytical outputs against engineering principles, domain knowledge and available evidence, investigating data quality issues and unexpected results. 

• Develop analytical methods and take ownership of their implementation, validation, and documentation. 

• Collaborate with data engineering, domain experts and software engineering teams to integrate analytical capabilities into customer-facing products. 

What We Are Looking For 

• A degree in Data Science, Computer Science, Engineering, Physics, Mathematics or a related quantitative discipline. 

• Experience in data science, advanced analytics or a closely related technical role. 

• Strong programming skills in Python and SQL. 

• Experience analysing complex datasets, ideally including sensor, time-series or operational data. 

• Ability to translate real-world engineering concepts or domain knowledge into analytical models, calculations and reusable logic. 

• Good understanding of statistical methods and quantitative problem-solving. 

• A structured approach to investigating data, testing assumptions and validating analytical results. 

• Ability to work effectively with engineering and domain experts and communicate technical concepts clearly. 

For senior-level candidates, we would additionally expect: 

• Experience developing analytical approaches to complex and sometimes ambiguous problems. 

• Ability to take ownership of analytical solutions from problem definition through to implementation and validation. 

• Experience designing robust, reusable analytical methods and making sound technical decisions. 

Nice to have 

• Experience in the energy sector, particularly electrical networks, power systems or electrical infrastructure. 

• Knowledge of electrical assets such as transformers, switchgear, substations or overhead lines. 

• Experience with asset monitoring, condition assessment, asset performance or predictive maintenance. 

• Experience working with sensor measurements, telemetry and time-series data. 

• Experience with anomaly detection, forecasting, machine learning or engineering-based analytical models. 

• Experience developing analytical capabilities that are integrated into software products. 

Our Values

• We work together
• We believe in people
• We won’t accept the ‘way it has always been done’
• We listen to learn
• We’re trying to do the right thing

Equal Employment Opportunity Statement

Individuals seeking employment at Camlin are considered without regards to race, colour, religion, national origin, age, sex, marital states, ancestry, physical or mental disability, gender identity or sexual orientation.