Build state-of-the-art predictive algorithms for sonnen’s global fleet of energy storage systems by leveraging new and existing data sources
Develop automated approaches to predictive maintenance by leveraging artificial intelligence / machine learning (AI/ML)
Gather and analyze data to optimize and improve existing products, to guide business strategies, and to discover potential new digital solutions
Build key components of ETL and analytics pipelines and deploy them into an operational analytics environment
Collaborate with data management and engineering on data quality and data governance issues
Collaborate with data engineers, other technology teams, and with business analysts from functional teams across all project phases from ideation to design, development, and product deployment
Requirements
2+ years of working experience in data science / machine learning
Master’s degree or PhD in Data Science, Computer Science, Mathematics, Physics, or another quantitative field
In-depth knowledge of data science and machine learning, with a strong foundation in statistics, time series analysis, forecasting, and/or optimization models
Proficiency with python, SQL, standard data science libraries (scikit-learn, pandas, torch/tensorflow), and cloud-computing and Big Data environments (AWS, Databricks, Spark)
Ability to gather and manipulate data, to draw insights from large data sets, and to put code into production
Knowledge of power systems, energy grids, or energy storage systems is a plus
A founder's mentality: ability to conceptualize and solve data problems and to quickly adapt to new challenges or topics