Head of Industrial Ai and Predictive Analytics

Alpha X is an innovative high-tech manufacturing technology company pioneering the integration of advanced automation, transportation, and AI-driven solutions to revolutionize traditional manufacturing processes. We harness the power of artificial intelligence, machine learning, and robotics to optimize production efficiency, enhance product quality, and foster sustainability across diverse high-tech industries.

**Responsibilities**:

- R&D Management: Evaluate the workload of algorithm development tasks for anomaly detection, classification, and prediction based on time-series structured data (e.g., temperature, low-frequency vibration, rotational speed, flow rate, etc.). Allocate human resources accordingly, manage development risks, ensure timely delivery of high-quality models, review code and ensure algorithm delivery quality. Continuously monitor performance and effectiveness to improve system capabilities. Coordinate across teams and departments, organize regular meetings, and initiate algorithm development status updates and information synchronization.
- Technology Selection: Provide recommendations for industrial AI algorithm selection and development in semiconductors manufacturing. Consider not only technical feasibility and advancement, but also align with product managers, sales, and other departments to assess business value from market and commercial perspectives.
- Technical Competitiveness: Conduct research on new technologies and competitor offerings to ensure the company’s industrial AI algorithms remain industry-leading and competitive.
- Algorithm Development: When required to contribute individually, design, develop, validate, test, deploy, and maintain algorithms for fault prediction projects and products.
- Intellectual Property Planning: Plan and research patents in fault prediction for semiconductor smart manufacturing, ensuring the company's algorithms are legally protected.
- Talent Development and Team Management: Build and develop the algorithm R&D team based on organizational goals and skill requirements. Identify gaps in team members' skills or capabilities and proactively coach and support their growth.Solution Consulting: Support pre-sales and sales in technical solution design. Act as a technical expert to communicate with clients and help close deals based on pre-sales/sales needs. Collaborate with business teams on product design and iteration to gain market recognition.

**Technical and Domain Requirements**:

- Educational Background: Master’s degree or above in Computer Science, Mathematics, Automation, Mechanical Engineering, or related fields.
- Experience: At least 5 years of hands-on experience in developing algorithms based on time-series structured data. Experience in process manufacturing is a plus.
- Machine Learning Skills: Proficient in common statistical methods (e.g., linear regression, PCA) and machine learning techniques (e.g., SVM, ANN, GBDT, Random Forest, LightGBM). Experience in combining signal processing feature engineering with machine learning algorithms is preferred.
- Multimodal Deep Learning Skills: Preferred experience in modeling time-series data using deep learning algorithms and deploying them in engineering environments. Proficiency in time-series forecasting methods (e.g., LSTM for prediction, AE for reconstruction, ARIMA for statistical analysis). Experience in building large time-series models from scratch is a plus. Familiarity with image-based object detection (YOLO, MobileNet), and unsupervised learning algorithms (GAN) is also preferred.
- Algorithm Engineering Skills: Experience with engineering projects involving parallel computation of algorithms; preference for those with experience using GPU to improve algorithm efficiency.
- Data Preparation Experience: Preferred experience in automated cleaning and management of large-scale time-series data. Experience in data augmentation, data quality enhancement, time-series segmentation, and change point detection using machine learning is a plus.
- Signal Processing Skills: Proficiency in commonly used signal processing methods for denoising and feature engineering, including:

- Time-domain methods: peak-to-peak value, RMS, kurtosis, Time Synchronous Averaging (TSA), etc.
- Frequency-domain methods: Fourier transform, frequency domain filtering, MFCC, Hilbert-Huang Transform, envelope spectrum analysis, etc.
- Time-frequency domain methods: Short-Time Fourier Transform (STFT), Wavelet Transform, Empirical Mode Decomposition (EMD), etc.
- Coding Ability: Proficient in using Python for machine learning and deep learning algorithm development. Familiar with mainstream Python libraries such as scikit-learn, PyTorch, TensorFlow, Keras, etc.

**Soft Skill Requirements**:

- Technical Management Skills: Able to assess technologies based on company strategy, product, and project needs, and create corresponding technical plans and roadmaps. Capable of leading the team to fulfill these goals.