Data Scientist – Analytics as a Service

Ralliant•Fairport, NY

1d•$104,300 - $193,700

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, failure prediction, and fleet benchmarking.
Design algorithms that are accurate, explainable and production-ready.
Extract insights from sensor data, time-series data, event logs, operational history, maintenance records, and customer operating conditions.
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.