Engineer- Machine Learning IV USA

First Solar reserves the right to offer you a role most applicable to your experience and skillset.

Basic Job Functions:

• The Machine Learning (ML) Engineer is responsible for leading the development, deployment, validation, and continuous improvement of models that accurately characterize the performance of First Solar photovoltaic products and systems. He/she will achieve results through leadership of cross-functional teams to identify research needs, develop and execute analysis and test plans, and ensure quality of data collection, analysis and reporting.
• The successful candidate will be a subject matter expert in the Machine Learning space, who is able to adapt quickly to new data and analytical requests from across the First Solar organization. This may include engineering statistical analysis, contract analysis, policy analysis and project management tasks.  The Machine Learning Engineer will also be responsible for orchestrating continuous integration of model artifacts to assist data scientists in generating detailed energy predictions for prospective and operating sites.
• The ideal candidate will build relationships with internal and external groups to research state of the art meteorological analysis, statistical analysis, energy prediction methods & tools, and recommendations for PV power plant design in support of their work. He/she will identify outstanding bottlenecks in the data infrastructure, support and enhance existing systems and productionize AI environments for modeling and forecasting. 

Education/Experience:

• Bachelor’s degree in computer science, Computer Engineering, Data Science, Electrical Engineering, Applied Statistics, or Physics required. Advanced degree is a plus.
• At least 10 years in Data Science, Artificial Intelligence or Machine Learning algorithm development.
• At least 5 years in building ML pipelines and prior experience with MLOps.

Required Skills/Competencies:

• Proficient with programming languages (Python, Matlab, R, or similar), relational databases (SQL Server), data analysis and visualization software's (preferably JMP, Power BI, SAS).
• Have solid understanding of the machine learning model stack (regression, classification, neural networks, time series) and ML Frameworks and libraries (Scikit learn, Xgboost, Keras, Pytorch, Tensorflow, statsmodels)
• Proficiency in managing and analyzing extensive sets of data, coupled with expertise in data scrubbing, ETL, feature extraction, and data visualization.
• Familiarity with renewables energy prediction modeling (preferably solar) and processing meteorological data and understanding of power engineering is preferred.
• Proficient with solar simulation software (PVSyst, PlantPredict, PV Sol, PV Design Pro or similar).
• Excellent communication, organization and interpersonal skills, comfortable to interpret data and modeling efforts to a large audience on a regular basis and publish results in reputed PV conference and journals.
• Strong self-direction, initiative, and ability to prioritize multiple tasks from various requestors, demonstrated ability to manage multi-faceted projects.
• Demonstrated results in developing and delivering customer-facing technical collateral.

Essential Responsibilities:

• Develop Artificial Intelligence/Machine Learning based algorithms such as energy prediction model, solar irradiance (GHI, POA) model, spectral correction model, device metastability model, and module degradation model to enable fast learning of product performance.
• Automate end-to end ETL and ML pipelines, deploy and maintain existing on-prem and Azure storage.
• Collaborate with data scientists and analytics team to deploy models, monitor performance and orchestrate continuous integration of model artifacts.
• Conduct data cleaning, preprocessing, and analysis of massive datasets to identify significant trends, ensuring that the data used for model training is of the highest quality. 
• Take ownership of data quality improvement in First Solar's field test sites. Assist in data analysis and state-of-the-art instrumentation and test equipment development. Forecast/Model long-term energy performance of First Solar modules in production/field environments, perform model vs actual analysis on a periodic basis. Identify areas of improvement in the models. Validate model predictions with measured data from plant monitoring systems and field test results.
• Evaluate the feasibility of new products, model energy performance of new module technologies under various meteorological climates, improve model through successive iterations, ensemble results to develop a robust model and validate model prediction with testbed before production deployment.
• Support methods for verifying the capacity of installe