Über
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. Data Analysis:
Expertly handle complex data sets, conduct in-depth data analysis, and derive actionable insights by applying advanced statistical and machine learning techniques. 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. Predictive Modeling:
Develop and deploy sophisticated machine learning models, utilizing algorithms like deep learning, ensemble methods, and neural networks to predict trends, behaviors, and outcomes. Data Visualization:
Create compelling data visualizations that effectively communicate complex findings and insights using tools like Tableau, Power BI, or custom Python visualizations. Feature Engineering:
Lead feature engineering efforts to identify and select critical data features, enhancing the predictive power of machine learning models. Statistical Validation:
Formulate, implement, and test hypotheses, providing robust statistical validation for key business decisions. Algorithm Development:
Lead the development of machine learning algorithms and their optimization to solve complex business problems. Lakehouse & Platform Implementation:
Implement end-to-end analytics workflows in the Nuclear 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 Integration:
Collaborate with IT and data engineering teams to integrate and access data from various sources, data lakes, and data warehouses. Model Deployment:
Oversee the deployment of machine learning models in production environments to support real-time decision-making and business applications. 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. Experimentation & A/B Testing:
Design and analyze A/B tests to measure the impact of changes, optimizations, and improvements. Data Ethics:
Ensure ethical data practices, privacy compliance, and adherence to data protection regulations in all data science initiatives. Collaboration & Technical Communication:
Partner with nuclear engineers, 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. Mentorship:
Provide mentorship and guidance to junior data scientists, fostering their growth and development. Strategic Leadership:
Act as a strategic leader, influencing data-driven culture across the organization, defining the data science roadmap, and contributing to long-term data strategy. Innovation:
Stay updated on the latest data science tools, techniques, and trends, continuously innovating and evaluating new technologies to improve data science practices. Education: Qualifications: Master's or Ph.D. in a quantitative field preferred (e.g., Computer Science, Statistics, Mathematics, Engineering). 10 to 15 years of experience in data science, including an extensive track record of implementing data solutions and driving data-driven decision-making. Proficiency in data analysis tools and programming languages such as Python, R, or Julia. Expert knowledge of machine learning algorithms and their applications. Exceptional skills in data visualization tools like Tableau, Power BI, or data visualization libraries in Python (e.g., Matplotlib, Seaborn). Profound understanding of databases and data manipulation using SQL. Outstanding problem-solving and critical thinking abilities. Strong leadership and communication skills, capable of conveying complex findings and insights to both technical and non-technical stakeholders. Extensive experience with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark). Expertise in data ethics, privacy, and compliance considerations. 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, nuclear, 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 Company standards for quality, accuracy, and auditability. Operate in accordance with nuclear safety culture and formal change management practices. Behavioral Attributes: Demonstrates Company values: Safety First, Act with Integrity, Intentional Inclusion, and Superior Performance. 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. Additional Requirements: This position may require compliance with applicable nuclear regulatory requirements, including background checks, testing, and training as required by policy. Work location and on-site requirements will be aligned with business needs.
Sprachkenntnisse
- English
Hinweis für Nutzer
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