Data Scientist

Data Scientist The Data Scientist is responsible for developing, validating, and deploying advanced analytical and machine learning solutions that support operational performance, equipment reliability, and economic outcomes. This role bridges complex telemetry and operational data with actionable insights delivered through a governed, cloud-native analytics platform. The position emphasizes production-grade implementation within an Azure Databricks Lakehouse environment and requires a strong commitment to documentation, auditability, and accuracy consistent with work in a highly regulated environment. This role works closely with engineering, IT, and analytics partners to translate operational needs into measurable analytical solutions that can be safely operated and maintained over time. Major Job Responsibilities Analytics & Model Development Develop, test, and maintain statistical, machine learning, and time-series models supporting use cases such as anomaly detection, predictive maintenance, and operational reliability analysis. Apply rigorous validation techniques to ensure models are explainable, reliable, and appropriate for operational decision support. Monitor model performance over time and support controlled updates as data and operational conditions evolve. Lakehouse & Platform Implementation Implement end-to-end analytics workflows in an Azure Databricks Lakehouse environment. Build and maintain data pipelines and feature datasets aligned with enterprise medallion architecture standards. Leverage scalable Spark-based processing for large and complex operational datasets. Use approved model lifecycle and experiment tracking practices to support repeatability and governance. Data Governance, Security & Compliance Design analytics solutions that adhere to data governance, access control, and auditability requirements. Ensure all analytical outputs are traceable to approved inputs and follow established data handling standards. Operate within platform security constraints intended to limit uncontrolled data ingress, egress, and access. Applied AI / Advanced Analytics Support the implementation of approved AI-enabled analytics and search capabilities, including retrieval-based techniques, where appropriate. Ensure AI-assisted solutions are verifiable, transparent, and aligned with enterprise and regulatory expectations. Collaboration & Technical Communication Partner with engineering, IT leaders, and platform teams to translate operational questions into analytical approaches. Participate in technical design and architecture discussions. Produce clear technical documentation to support long-term maintenance and operational support. Education Master’s degree in Data Science, Computer Science, Engineering, Physics, Statistics, or a related quantitative discipline preferred. Required Knowledge & Skills Strong proficiency in Python for data analysis and model development. Solid experience with SQL and working with large-scale structured datasets. Demonstrated experience building analytics or machine learning solutions in a production cloud environment. Strong foundation in statistical modeling and time-series analysis. Ability to clearly explain analytical methods, assumptions, and limitations. Desired / Preferred Knowledge & Skills Experience supporting analytics in energy, utilities, or other highly regulated industries. Familiarity with industrial or operational time-series data. Experience contributing to MLOps practices, including repeatable training and deployment workflows. Understanding of enterprise data governance and access control concepts. Documentation & Standards Produce comprehensive documentation for analytical solutions, including model intent, assumptions, validation results, and operational considerations. Ensure all work aligns with enterprise standards for quality, accuracy, and auditability. Operate in accordance with formal change management and safety-focused operational practices. Behavioral Attributes Demonstrates a strong commitment to safety, integrity, inclusion, and performance excellence. Strong attention to detail and analytical rigor. Effective collaborator across technical and operational teams. Comfortable working in environments with formal controls, documentation expectations, and regulatory oversight. Saxon Global