Data Scientist – Analytics as a Service
About The Position
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.
Requirements
- 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
Nice To Haves
- Industrial AI
- Utilities
- Rotating machinery
- Power systems
- Time-series analytics
- Azure Machine Learning
- AWS SageMaker
- MLOps
- LLMs and Generative AI
Responsibilities
- Develop advanced analytics for rotating machine condition monitoring, grid monitoring, predictive maintenance, fault detection, anomaly detection, asset health assessment, and failure prediction.
- Design algorithms that are accurate, explainable and production-ready.
- Extract insights from sensor data, time-series data, event logs, operational history, and maintenance records.
- Develop robust feature engineering pipelines to improve model accuracy and scalability.
- Develop and optimize machine learning models, statistical models, generative AI applications, Large Language Model integrations, predictive analytics, and recommendation engines.
- Leverage AI-assisted tools to accelerate experimentation, model development and validation.
- Deploy models into production.
- Monitor model performance.
- Improve inference accuracy.
- Reduce computational costs.
- Continuously retrain models.
- Ensure analytics are scalable, reliable and maintainable.
- Understand customer use cases and translate them into differentiated analytics capabilities.
- Use customer feedback and operational data to continuously improve algorithms and business outcomes.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
