Job description
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.
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