Battery Energy Storage System Machine Learning Engineer

Key Skills

Aartificial intelligence, Algorithms, AWS, Azure, Cloud computing, Data pipelines, Data science techniques, Git, Kubeflow, Kubernetes-K8s, Machine learning techniques, MLflow, Python Programming, PyTorch, R Programming, TensorFlow

Job Description

Looking for challenging role? If you really want to make a difference - make it with us

Siemens Energy is focused on helping customers navigate the world’s most pressing energy problems.

As a world leader in developing and producing the most advanced engineering technologies, we improve lives and further human achievements worldwide, while also protecting the climate - all thanks to our employees.

Your new role – challenging and future-oriented

The position presents a unique opportunity to improve and transform the current state-of-the-art Battery Energy Storage Systems (BESS) Digitalization and Analytics. You will, together with dedicated international colleagues, get the chance to work with and build up a world-class BESS Digitalization R&D group. We are looking for flexibility, curiosity, and eagerness to learn in the candidate. You will get the opportunity to have a tangible impact on our success story. This position requires a creative and analytical mindset, a strong understanding of data analysis, modern machine learning tools, algorithms and pipelines, and a solid knowledge base in time-series modelling, dynamic systems and energy storage systems. The candidates will be able to develop their skills and knowledge on two highly demanded topics: data analysis and battery energy storage technology.

• Developing foundational ML/AI models, algorithms, and solutions involving BESS that can be vetted in experimental setups.
• Develop requirements for performance prediction models and algorithms based on time series data.
• Develop, simulate and verify battery state algorithms (SoX) for battery cells, modules, and packs based on voltage, temperature and electrochemical impedance data obtained from field and test set-up. The SoX estimation includes state-of-charge, state-of-health and state-of-power.
• Integrating new forecasting algorithms into our production code base with robust test coverage.
• Perform feature engineering and benchmark performance across various load profiles.
• Investigate the optimum partitioning of algorithms between constrained edge, cloud and hybrid configuration.
• Working with stakeholders to understand and translate the domain drivers into requirements for the AI solutions in the BESS domain.
• Working with domain engineers to identify the improvement areas in the currently available techniques.
• Proactively identify opportunities within Siemens Energy’s BESS R&D group that can benefit from data science analysis and present those findings.
• Collaborating and coordinating with field test engineers to evaluate and optimise different test procedures, i.e., HPPC, OCV, thermal and calendar/cycling life tests, and design Li-ion cells/packs to meet desired model parameterization, characterization and performance requirements.
• Support the hardware and software teams on algorithm implementation, integration, testing and verification.
• Managing technical meetings and drafting and presenting reports.
• Gain in-depth experience in an exciting industry as you work with storage sizing, energy financial models, energy tariffs, storage controls & monitoring.

We don’t need superheroes, just super minds

• A Master’s or PhD degree in electrical engineering/electronic engineering/ computer science or similar numerical-intensive fields.
• Knowledge of mathematical foundations of statistics and statistical signal processing, dynamic systems, and state estimation algorithms.
• Hands-on experience and in-depth knowledge of probabilistic programming, Bayesian learning, optimisation, deep learning, and other advanced machine learning techniques are highly preferred.
• Good data analysis and scripting skills to process and analyze large volumes of data.

Expected Skills:

• You have a passion for time-series forecasting and experience bringing time-series forecasts to production.
• Full stack/machine learning engineering experience.
• Programming skills in Python/R/TensorFlow/Pytorch/PyMC etc., and its broader numerical ecosystem.
• At least 2-3 years of commercial experience developing production-ready real-time projects in machine learning and data science.
• At least 1-2 years of experience with cloud technologies Azure/AWS/Google Cloud and tech stack, Kubernetes, Kubeflow, MLFlow, or any other data science/ML pipeline or platforms.
• AzureML, AWS, terraform, continuous integration, monitoring and alerting.
• Extra credit if you can demonstrate the following:
• Exposure to energy markets or battery systems modelling, knowledge of Li-ion battery electrochemistry, design of experiments (DOE), parameter optimisation.
• Knowledge or experience in state estimation algorithms for Lithium-ion batteries
• You should be able to work as an individual contributor (IC role).
• You must have a customer focus and practical stakeholder management skills.
• Version control code (Git), including dev, test and production environments.

Other expected traits:

• Creativity and analytical mindset
• Quick learner and detailed oriented.
• Working collaboratively in a diverse environment. We commit to reaching better decisions by respecting opinions and working through disagreements.
• Excellent communication skills with an ability to present solutions to senior management.