Role Overview

We are seeking a Data Scientist to help build the next generation of industrial intelligence for our operations, reliability, maintenance, and performance optimization. This role sits at the intersection of applied machine learning, large-scale industrial telemetry, physics-informed analytics, and cloud software platforms.

You will develop and productionize advanced AI/ML models that transform high-frequency operational turbine data into actionable customer intelligence — reducing forced outages, improving availability, lowering O&M costs, and enabling predictive operations across fleets of industrial assets.

Key Responsibilities

Machine Learning

• Design, develop, and deploy machine learning models for:
• Predictive maintenance, Anomaly detection, Failure prediction, Remaining useful life (RUL) estimation, Operational optimization, Fleet-wide analytics
• Build and train models using large-scale industrial telemetry and operational datasets.
• Apply advanced ML techniques including:
• Time-series forecasting, Deep learning, Statistical modeling, Unsupervised learning, Physics-informed ML approaches
• Develop algorithms capable of handling noisy, sparse, and real-world operational data.
• Evaluate model performance using operational KPIs and real-world production feedback.

Production ML & MLOps

• Build scalable production pipelines to operationalize ML models into customer-facing products.
• Develop infrastructure for:
• Feature engineering, Automated retraining, Model monitoring, Drift detection, Experiment tracking, CI/CD for ML workflows
• Deploy models across cloud and edge-computing environments.
• Collaborate closely with software engineering teams to integrate ML capabilities into SaaS applications and operational workflows.

Cross-Functional Collaboration

• Partner with controls engineers, reliability engineers, product managers, and software teams to solve complex industrial problems.
• Translate operational challenges into scalable data science solutions.
• Communicate technical findings and recommendations to both technical and non-technical stakeholders.
• Contribute to technical strategy and mentor junior engineers and data scientists.

Required Qualifications

• Bachelor’s in Computer Science, Data Science, Statistics, Engineering, Physics, Applied Mathematics, or related quantitative field.
• 3+ years of experience in machine learning, applied AI, or production data science systems.
• Strong proficiency in:
• Python, SQL, Scientific computing and data engineering workflows
• Experience with modern ML frameworks and tools such as:
• PyTorch, TensorFlow, Scikit-learn, XGBoost, Spark
• Experience building and deploying production ML systems in cloud environments (AWS, Azure, or GCP).
• Strong understanding of:
• Time-series analytics, Statistical inference, Feature engineering, Distributed systems, Production software engineering practices
• Experience with containerization and orchestration tools such as Docker and Kubernetes is a plus.

Preferred Qualifications

• Experience in industrial systems, IIoT, energy, power generation, aerospace, or reliability engineering.
• Familiarity with:
• Data Streaming platforms (Azure/AWS/GCP services), MLflow, Real-time analytics systems
• Experience deploying ML systems in operationally critical or high-availability environments.
• Knowledge of digital twins, edge AI, or physics-informed machine learning techniques.