Machine Learning/MLOps Engineer - Battery Energy Storage Systems

Key Skills

AI technology, Cloud computing, Data Analysis, Kubeflow, Kubernetes-K8s, Machine learning techniques, MLflow, PyMC3, Python Programming, PyTorch, R Programming, SQL, TensorFlow

Job Description

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

Due to their high versatility and decreasing costs, Battery Energy Storage Systems (BESS) are becoming a significant technology in transforming electric networks. With energy ratings touching up to several hundreds of MWh, these large BESS generate massive data via many control layers and sensors spread all over the BESS systems. These sensors measure various parameters, from electrical to thermal ones and parameters for self-diagnosis of the system's sub-components and control laws. However, converting this large amount of data into useful information is challenging to get a synthetic yet comprehensive picture of BESS's health and operational behavior. In this context, Siemens Energy R&D is building capabilities to exploit these BESS through the analysis of the operational data with the objectives to check the level of safety of the assets, anticipate the maintenance needs, follow the performance, and eventually extend the lifetime of the projects.

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. The candidates will be able to develop their skills and knowledge on two highly demanded topics: ML/AI and battery energy storage technology.

Responsibilities:

• 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, and integrate data-driven, hybrid battery state estimation and forecasting algorithms for battery cells, modules, packs and other assets in a BESS system based on data obtained from field projects and test set-up.
• Perform feature engineering and benchmark performance across various load profiles.
• Integrate new algorithms into production ready data and ML pipelines base with robust test coverage.
• 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.
• 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 a PhD degree in electrical engineering/electronic engineering/ computer science or similar numerical-intensive fields.
• Knowledge of mathematical foundations of statistics and statistical signal processing, good data analysis and scripting skills to process and analyze large volumes of data.
• At least 2-3 years of commercial experience developing production-ready real-time projects in machine learning and data science.
• Hands-on experience and in-depth knowledge of probabilistic programming, Bayesian learning, optimization, deep learning, and other advanced machine learning techniques are highly preferred.
• Time-series forecasting and experience bringing time-series forecasts to production.
• Strong programming skills in Python/R/TensorFlow/Pytorch/PyMC etc., and its broader numerical ecosystem.
• At least 2 years of hands-on experience with cloud technologies Azure/AWS/Google Cloud and tech stack, Kubernetes, Kubeflow, MLFlow, or any other data science/ML pipeline or platforms.
• At least 2 years AzureML, AWS, terraform, continuous integration, monitoring and alerting.
• Version control code (Git), including dev, test and production environments.
• You should be able to work as an individual contributor (IC role).
• You must have a customer focus and practical stakeholder management skills.
• 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.

We’ve got quite a lot to offer. How about you?

This role is based at Site ( Gurgaon ). You’ll also get to visit other locations in India and beyond, so you’ll need to go where this journey takes you. In return, you’ll get the chance to work with teams impacting entire cities, countries – and the shape of things to come.