Data Scientist

Role OverviewWe 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 ResponsibilitiesMachine LearningDesign, develop, and deploy machine learning models for:Predictive maintenance, Anomaly detection, Failure prediction, Remaining useful life (RUL) estimation, Operational optimization, Fleet-wide analyticsBuild 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 approachesDevelop algorithms capable of handling noisy, sparse, and real-world operational data.Evaluate model performance using operational KPIs and real-world production feedback.Production ML & MLOpsBuild 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 workflowsDeploy 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 CollaborationPartner 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 QualificationsBachelor’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 workflowsExperience with modern ML frameworks and tools such as:PyTorch, TensorFlow, Scikit-learn, XGBoost, SparkExperience 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 practicesExperience with containerization and orchestration tools such as Docker and Kubernetes is a plus.Preferred QualificationsExperience in industrial systems, IIoT, energy, power generation, aerospace, or reliability engineering.Familiarity with:Data Streaming platforms (Azure/AWS/GCP services), MLflow, Real-time analytics systemsExperience deploying ML systems in operationally critical or high-availability environments.Knowledge of digital twins, edge AI, or physics-informed machine learning techniques.