Data Scientist – Analytics as a Service

Data Scientist – Analytics as a Service Position Summary The Senior Data Scientist is responsible for developing the advanced analytics, machine learning models and AI algorithms that power Qualitrol's Analytics as a Service portfolio. Working closely with the Product Owner, Product Engineers and Data Engineer, this individual transforms industrial data into scalable analytics services that deliver measurable customer value. Unlike a traditional research-oriented data science role, this position is expected to rapidly move algorithms from experimentation into production, continuously improving model performance through customer feedback, operational data and AI-assisted development practices. Success requires balancing scientific rigor with startup execution speed. Primary Responsibilities Analytics & Model Development Develop advanced analytics for: Rotating machine condition monitoring Grid monitoring Predictive maintenance Fault detection Anomaly detection Asset health assessment Failure prediction Fleet benchmarking Design algorithms that are accurate, explainable and production-ready. Data Mining & Feature Engineering Extract insights from: Sensor data Time-series data Event logs Operational history Maintenance records Customer operating conditions Develop robust feature engineering pipelines to improve model accuracy and scalability. AI & Machine Learning Develop and optimize: Machine learning models Statistical models Generative AI applications Large Language Model integrations Predictive analytics Recommendation engines Leverage AI-assisted tools to accelerate experimentation, model development and validation. Production Deployment Partner with Product Engineers to: Deploy models into production Monitor model performance Improve inference accuracy Reduce computational costs Continuously retrain models Ensure analytics are scalable, reliable and maintainable. Customer Value Creation Partner with Product Owner and Customer Success to understand customer use cases and translate them into differentiated analytics capabilities. Use customer feedback and operational data to continuously improve algorithms and business outcomes. Required Experience Master's or Ph.D. in Data Science, Computer Science, Statistics, Applied Mathematics or related field 5+ years developing machine learning or industrial analytics solutions Strong Python programming experience Experience with cloud-based ML environments Experience deploying production AI models Strong statistical and analytical skills Preferred Experience Experience with: Industrial AI Utilities Rotating machinery Power systems Time-series analytics Azure Machine Learning AWS SageMaker MLOps LLMs and Generative AI Success Measures Within 12 months: Multiple production analytics models deployed Measurable improvement in prediction accuracy Repeatable MLOps pipeline established Analytics capabilities contributing to customer adoption Continuous model improvement process operational #LI-PW1