Senior Data Engineer

Responsibilities Model Development: Design and implement advanced machine learning models for predictive maintenance, anomaly detection, and computer vision-based quality control. End-to-End Pipeline Construction: Architect data pipelines from ingestion (sensor data, PLC logs) to model deployment and monitoring using GCP and Python. Statistical Analysis: Apply rigorous statistical methods to identify patterns in manufacturing data that correlate with vehicle quality or equipment downtime. Cross-Functional Collaboration: Partner with Product and Engineering teams to translate manufacturing pain points into technical requirements and deliver user-centric data products. Technical Leadership: Act as a subject matter expert within ATP, conducting code reviews, mentoring junior scientists, and staying at the forefront of AI/ML research in the industrial space. Scalability: Ensure models are optimized for production environments, moving from localized pilots to global plant-wide deployments. Data Strategy: Work with data engineering to improve data collection protocols and sensor telemetry quality from the plant floor. Requirements Education: Requires a bachelor’s or foreign equivalent degree in computer science, information technology or a technology related field Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field. Experience: 5+ years of professional experience in a Data Science role, with a proven track record of deploying models into production environments. Technical Stack: Proficiency in Python (R and SQL are also highly valued). Expertise in machine learning frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost, and LightGBM. Experience working within Google Cloud Platform (GCP) (Vertex AI, BigQuery, Dataflow). Domain Knowledge: Previous experience with time-series analysis, industrial IoT data, or manufacturing quality systems. Preferred Requirements: Advanced Degree: PhD in a relevant field. Deep Learning: Experience with Computer Vision (CNNs) for automated inspection or Transformers for complex sequence modeling in sensor data. MLOps: Familiarity with CI/CD for machine learning, containerization (Docker/Kubernetes), and model monitoring tools. Communication: Ability to explain complex mathematical concepts to non-technical stakeholders (e.g., plant managers and design leads). Problem-Solving: A "product-first" mindset—focusing on the business impact of the model rather than just its accuracy metrics. #J-18808-Ljbffr Jobtailor