Wind Energy Subject Matter Expert – Machine Learning and Data Engineering
Technical/Research Team | Location Open to Discussion
Sentient Corporation is a software company offering DigitalClone solutions to dramatically reduce fatigue, friction, and wear-based product issues with computational testing design simulations and prognostic asset management. Solutions are offered that leverage Computational Material Science, Tribology, advanced Statistics, Big Data management, and Information Fusion for accurate predictive assessments of asset health. Sentient differentiates itself through combining multi-scale physics-based material models, from the material microstructure to the component, such as bearings or gears, to full systems in wind turbines, automotive and rotorcraft industries
Sentient is looking for a wind energy subject matter expert (SME) with an extensive technical/scientific background in wind energy domain and proven expertise in machine learning (ML) and AI. The ideal candidate will be able to independently select, optimize and implement machine learning (and as needed, deep learning) methods to extract insights and build models to augment Sentient software capabilities – the results of these advancements will directly lead to reductions in Levelized Cost of Wind Energy (LCOE) rendering wind energy to be more competitive and helping make this world a better place.
This role will be positioned in our Wind Engineering Analytics team and will have direct access to large repositories of data streams from operational wind farms and needs to be very competent in working with structured data and unstructured data in variety of formats. Compensation will be commensurate with experience.
- Analyze and extract features from large repositories of quality-controlled wind turbine operational data time-series and event logs
- Select/Curate/Develop ML models from derived features and expert wind SME expertise. Develop and implement ML algorithms for specific wind turbine sub-sytem assemblies
- Work with other SMEs/scientists across the company to scientifically validate any developed models
- Attend conferences, deliver presentations/papers, build relationships and leverage accumulated knowledge and network to drive further advancements to our data engineering & ML capability
- Support other in-house ML and deep-learning initiatives as necessary.
- Subject matter expertise related to performance and reliability of wind turbines or other similar industrial internet platforms
- 5+ years of experience working with time-series data, logs etc. from SCADA systems and wind power meteorology datasets – prove that you have worked directly with real-world messy data!
- Experience with optimizing and implementing variety of machine learning methods for feature extraction, training, prediction and validation
- Proficient in writing efficient code using statistical/ML tools-of-the-trade: e.g. Python, R, MATLAB
- Experience working SCADA/HMI historians (e.g. PI) and other SCADA aggregation databases.
- Clear communication skills to interface effectively with folks from different backgrounds (software, other engineering, business).
- Last but not the least – strong problem solving and troubleshooting skills.
- Exposure and understanding of deep-learning techniques (e.g. ANN, LSTM, SDAE)
- Experience working with SCADA data stored in relational and/or Big Data platforms and familiarity with related technologies is a plus.
- Experience with SQL programming and data warehousing systems in an enterprise setting.
- Experience with shell scripts (bash, csh, etc.) in a Linux cloud environment.
About the Company:
Sentient Science Corporation is a leader in predicting the durability and reliability of complex rotating equipment. Sentient Science’s cloud-based software, DigitalClone Live, manages the health and life extension of rotating mechanical equipment using a materials science-based approach. The technology models and simulates components and full systems within the aerospace, rail and wind energy industries. DigitalClone applies materials science and physics-based modeling techniques to simulate rotating equipment under representative operational loads and conditions. Operators optimize their maintenance cycles and lower the pre-and post-installation costs of “rotating” systems through life extension actions. Equipment manufacturers use the software for design tradeoff and sensitivity analysis and to prove their life claims to the market. Sentient Science was recognized by the White House in 2014 with the Tibbetts Award and Bloomberg New Energy Finance Pioneers Award in 2016.
Please submit your Résumés and Cover Letter to [email protected].
(We DO NOT accept résumés from third party recruitment agencies)