About

• Background in data science, machine learning, and AI methodologies. Proficiency in time series analysis and related algorithms. Experience with Python and relevant libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch). Experience with Apache Spark (pyspark), Jupyter, Linux, SQL(PostgreSQL). Familiarity with data visualization techniques and tools, such as Grafana. Basic understanding of clustering techniques and statistical methods. Knowledge of power consumption and temperature measurement data from electronic hardware products is a plus. Ability to work independently and systematically, with a problem-solving mindset.