الوصف الوظيفي
You will be a key driver in our transition from reactive repairs to a proactive service model. By leveraging high-frequency instrument telemetry and service data, you will build the predictive analytics directly impacting how we support our global customer base. Key Responsibilities Proactive Solution Development: Design and deploy ML models to predict component failures and estimate Remaining Useful Life (RUL). Anomaly Detection: Build robust algorithms to detect silent deviations in machine performance from high-frequency sensor and log data. Data Product Integration: Collaborate with the team to consume and refine data products from Snowflake and Databricks. End-to-End ML Pipelines: Develop, test, and scale ML pipelines on Databricks/Snowflake. Scalable MLOps: Own the end-to-end lifecycle of the models—from experimentation in notebooks to production deployment and monitoring using MLflow. Actionable Insights: Work with domain experts to ensure model outputs are not just "scores," but clear, actionable steps for field engineers. GenAI Collaboration: Support the integration of predictive insights into our GenAI-solutions, helping provide context-aware troubleshooting steps based on model outputs. Technical Skills The Essentials: Mastery of Python and SQL. Proficiency in PyTorch/TensorFlow. ML Foundations: Proven experience with classical ML (XGBoost, Random Forest, Scikit-learn) applied to Time-Series or Sensor data. Predictive Modeling: Strong understanding of Time-Series Analysis, Survival Analysis, and Anomaly Detection (e.g., Isolation Forests, Autoencoders) Big Data Ecosystem: Hands-on experience with Databricks/Spark for processing large-scale machine datasets. Statistical Depth: Familiarity with Survival Analysis or reliability engineering concepts is a strong plus. Nice to Have Experience with Deep Learning architectures (e.g., LSTMs or GRUs) for sequential data. Familiarity with GenAI/LLM integration (building tools or agents). Knowledge of dbt for data modeling within the ML pipeline.
لقد تمت ترجمة هذا الإعلان الوظيفي بواسطة الذكاء الاصطناعي وقد يحتوي على بعض الاختلافات أو الأخطاء البسيطة.
