Для работы 1. Audio Processing: Look for someone experienced in audio processing techniques, such as audio file parsing, audio feature extraction, and audio signal processing. 2. Feature Extraction: They should have knowledge of various audio features like spectrograms, MFCCs (Mel-frequency cepstral coefficients), or other domain-specific features that can represent music characteristics. 3. Deep Learning: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other architectures commonly used in audio analysis. They should have experience in training and fine-tuning models for audio-related tasks. 4. Machine Learning Tools and Libraries: Proficiency in popular machine learning tools and libraries such as Python, TensorFlow, PyTorch, or Keras. 5. Data Storage and Processing: Familiarity with databases and data processing frameworks like SQL, NoSQL, Apache Spark, or Apache Hadoop will be beneficial for managing and manipulating large volumes of data efficiently.