Data Scientist

The Data Scientist makes predictive insights and builds decision-support tools from operational and performance data, combining hands-on data science (statistical modeling and machine learning) with ownership of industrial analytics stack. The Data Scientist serves as a PI System Administrator, Seeq Developer, and builder of AI-enabled tools that help engineers and operators act faster and with confidence.

The Data Scientist will work with industrial data systems-especially the PI System (OSIsoft PI / AVEVA PI)-to ensure reliable data availability and governed context and will develop Seeq analyses and Python-based pipelines for time-series modeling, anomaly detection, forecasting, and performance monitoring.With curiosity, rigor and comfort with large time-series datasets the Data Scientist is able to take solutions end-to-end-from PI tag and context configuration, to Seeq development, to Python/AI tool deployment-while collaborating with cross-functional technical teams.

Essential Functions

• Data Science, Modeling, and Insights

• • Build statistical and machine-learning models to detect anomalies, forecast performance, and identify optimization opportunities
• • Design and evaluate experiments and model validation approaches; translate results into clear recommendations for engineering and operations
• • Develop dashboards, reports, and model performance metrics to communicate insights and drive data-informed decisions

• PI System Administration & Time-Series Data Engineering

• Administer and support the PI System (OSIsoft PI / AVEVA PI), including tag strategy, data quality monitoring, and user support
• Build and maintain PI AF structure (assets, templates, attributes) and documentation to provide governed context for analytics and reporting
• Support PI interfaces/data flows and collaborate with OT/IT and engineers to validate sensors/tags, troubleshoot gaps, and improve reliability and performance
• Create curated datasets, features, and labels from PI data (with clear definitions and lineage) to support Seeq analyses and ML modeling

• Seeq Development & AI-Enabled Tools

• Develop and maintain Seeq Workbooks/Analyses for performance monitoring, anomaly detection, and root-cause investigations
• Create reusable Seeq templates, calculation standards, and best practices; enable users through documentation and training
• Build AI-enabled tools (e.g., copilots, guided diagnostics, automated summaries) that leverage governed PI/Seeq context to accelerate engineering workflows
• Evaluate, monitor, and improve AI tool quality (accuracy, drift, user feedback), and implement practical guardrails for safe, reliable use

• Python, Analytics Engineering & Deployment

• Develop and maintain Python-based pipelines for data extraction, preprocessing, modeling, and automation
• Prototype and productionize analytical applications that support performance monitoring, anomaly detection, and forecasting
• Automate recurring model runs, evaluations, and reporting workflows with attention to reproducibility and reliability
• Improve existing analytics codebases; contribute to model monitoring, documentation, and maintainable data science practices

• Project & Engineering Partnership

• Collaborate with engineers and subject matter experts to frame operational problems into measurable data science objectives
• Provide analytical support for initiatives including data validation, statistical analysis, modeling, and performance reporting
• Help standardize modeling approaches, feature definitions, and evaluation metrics across projects

• Data Quality, Governance & Monitoring

• Ensure accuracy and reliability of datasets used for analysis and modeling (validation checks, outlier handling, sensor sanity checks)
• Perform data cleaning, validation, and documentation, including assumptions, feature definitions, and dataset lineage
• Maintain organized analytical workflows and pipelines to support repeatable modeling and ongoing monitoring

Other Responsibilities

• Other duties and projects as assigned by management

• Bachelor's degree in Data Science, Computer Science, Engineering, Statistics, Applied Math, or related field

• 2-5 years of experience in data science, applied analytics, or technical modeling roles

• Strong Python skills for data science (e.g., pandas, numpy, scikit-learn; visualization libraries)

• Strong skills in SQL and Excel for analysis, validation, and stakeholder-ready outputs

• Experience with data visualization and reporting tools (Power BI, Tableau, or similar)

• Strong statistical reasoning, analytical problem-solving skills, and attention to data quality

• Ability to communicate technical findings clearly to both technical and non-technical stakeholders

• Demonstrated experience using applied statistics, machine learning, and time-series modeling

• Ability to use Python for data science and AI tooling (data wrangling, modeling, visualization; building assistants/automation)

• Understand and apply PI System administration fundamentals and data engineering for high-frequency time-series (tags, quality checks, contextualization)

• Seeq development (shared analyses, calculations, templates) and stakeholder-ready data storytelling

• Ability to partner cross-functionally and tool and team enablement (requirements, training, documentation, adoption)

• Lead and support deployment-minded practices (reproducibility, versioning, testing, monitoring) for analytics, models, and AI tools

• Must have strong verbal and written communication skills

• Must be able to read, write and speak English at a level which will permit the employee to accurately understand and communicate information to safely and efficiently perform the job duties

• Ability to prioritize and plan work activities so time is used efficiently and effectively

• Must demonstrate accuracy and thoroughness to ensure quality performance

• Ability to identify and resolve problems in a timely manner, + Experience working with OSIsoft PI / AVEVA PI System (PI Data Archive, PI AF) and industrial time-series data

• Experience developing in Seeq (Workbench/Organizer), including building shared analyses and calculations
• Experience with operational/engineering datasets (e.g., power generation, rotating equipment, process systems)
• Familiarity with time-series methods (e.g., resampling, lag features, seasonality, change-point detection)
• Experience developing reusable analytics packages, APIs, or scheduled jobs for model execution
• Knowledge of predictive modeling (forecasting, classification/regression), anomaly detection, and model evaluation

Physical Requirements

• The ability to work in an office environment and to work at a computer, and computer monitor, and use repetitive motion for long periods of time

• The ability to periodically lift up to 15 lbs