Energy Data Scientist: Predictive Analytics & ML

Shell Energy Operations Pty Ltd; City of Melbourne; On-site; AUD 120,000 - 170,000; Full time

Job summary

Shell Energy in Melbourne is seeking a Data Scientist to join the Digital & Technology Solutions team. You will build and maintain data science-based products that support Shell Energy’s strategy, improve operational efficiency and drive business growth, reporting to the Lead Data Scientist.

Responsibilities include designing advanced analytics and machine learning models, collaborating with data engineers and stakeholders, applying DevOps principles, and monitoring model performance to ensure

Qualifications

• Tertiary qualification in data science or related field.
• Experience deploying ML models in a commercial environment.
• Proficient in end-to-end model development lifecycle.
• Knowledge of forecasting, optimisation, or energy analytics is a plus.

Responsibilities

• Develop, deploy and maintain data science-based products that support Shell Energy’s strategic objectives and business performance.
• Design and implement advanced analytics and machine learning models to solve complex business problems.
• Work with stakeholders to translate business requirements into data-driven solutions.
• Apply DevOps principles to support ongoing operation and maintenance of data science products.
• Monitor, evaluate and improve model performance, ensuring accuracy and fit for purpose.

Skills

Data science

Machine learning

Python

SQL

Stakeholder engagement

DevOps

Critical thinking

Education

Data Science degree

Tools

Python

R

SQL

TensorFlow

Spark

Job description

Shell Energy in Melbourne is seeking a Data Scientist to join the Digital & Technology Solutions team. You will build and maintain data science-based products that support Shell Energy’s strategy, improve operational efficiency and drive business growth, reporting to the Lead Data Scientist.

Responsibilities include designing advanced analytics and machine learning models, collaborating with data engineers and stakeholders, applying DevOps principles, and monitoring model performance to ensure