Domain Expert Condition Monitoring for Electrical Equipment

About the role:

We are seeking a highly skilled Domain Expert in Condition Monitoring to join our team and play a pivotal role in advancing predictive maintenance strategies for electrical equipment. This position focuses on leveraging cutting-edge machine learning and data analytics techniques to design and implement scalable solutions that optimize maintenance processes, enhance equipment reliability, and support operational efficiency.

As part of this role, you will apply your expertise in predictive modeling, supervised and unsupervised learning, and advanced data analysis to uncover actionable insights from high-dimensional datasets. You will collaborate with cross-functional teams to translate business requirements into data-driven solutions that surpass customer expectations. If you have a passion for innovation and sustainability in the industrial domain, this is an opportunity to make a meaningful impact.

Key Responsibilities:

• Develop and implement predictive maintenance models using a variety of supervised and unsupervised learning techniques.
• Analyze high-dimensional datasets to identify patterns and correlations that can inform maintenance strategies.
• Utilize linear methods for regression and classification, as well as advanced techniques such as splines, wavelets, and kernel methods.
• Conduct model assessment and selection, focusing on bias, variance, overfitting, and cross-validation.
• Apply ensemble learning techniques, including Random Forest and Boosting, to improve model accuracy and robustness.
• Implement structured methods for supervised learning, including additive models, trees, neural networks, and support vector machines.
• Explore unsupervised learning methods such as cluster analysis, principal component analysis, and self-organizing maps to uncover insights from data.
• Engage in directed and undirected graph modeling to represent and analyze complex relationships within the data.
• Collaborate with cross-functional teams to translate business requirements into data-driven solutions.
• Communicate findings and insights to stakeholders, providing actionable recommendations for maintenance optimization.

Mandatory Requirements:

• Master’s degree or Ph.D. in Data Science, Statistics, Computer Science, Engineering, or a related field.
• Proven experience in predictive modeling and machine learning, particularly in the context of predictive maintenance.
• Strong programming skills in languages such as Python, R, or similar, with experience in relevant libraries (e.g., scikit-learn, TensorFlow, Keras).
• Familiarity with data visualization tools and techniques to effectively communicate complex data insights.
• Experience with big data technologies and frameworks (e.g., Hadoop, Spark) is a plus.
• Excellent problem-solving skills and the ability to work independently as well as part of a team.
• Strong communication skills, with the ability to convey technical concepts to non-technical stakeholders.

Good to Have:

• Experience in Industrial software & Enterprise solutions

Preferred Skills & Attributes:

• Strong understanding of modern software architectures and DevOps principles.
• Ability to analyze complex problems and develop effective solutions.
• Excellent communication and teamwork skills, with experience in cross-functional collaboration.
• Self-motivated and capable of working independently on complex projects.

About the Team

Become a part of our mission for sustainability: clean energy for generations to come. 

We are a global team of diverse colleagues who share a passion for renewable energy and have a culture of trust and empowerment to make our own ideas a reality.

We focus on personal and professional development to grow internally within our organization.

Who is Siemens Energy? 

At Siemens Energy, we are more than just an energy technology company. We meet the growing energy demand across 90+ countries while ensuring our climate is protected. With more than 96,000 dedicated employees, we not only generate electricity for over 16% of the global community, but we’re also using our technology to help protect people and the environment. 

Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.