Operations Data Scientist

Include but are not limited to:

• Develop and maintain predictive, statistical, and reliability models using operational, production, and maintenance data to support performance optimization and risk reduction.
• Analyze structured and unstructured data to identify trends, anomalies, and opportunities for operational improvement.
• Perform forecasting, scenario modeling, and techno-economic analysis to support planning, prioritization, and capital allocation decisions.
• Design and support proactive monitoring tools (e.g., Operate-by-Exception workflows) to identify high-risk assets and emerging operational issues.
• Build and maintain dashboards, reports, and executive-level summaries using business intelligence tools.
• Write and manage SQL queries, data transformations, and analytical data models within cloud-based data platforms.
• Collaborate with cross-functional teams (Operations, Engineering, Maintenance, Reliability, IT, and Finance) to define requirements and deliver scalable data solutions.
• Support data integration, data quality, and governance to ensure accurate, consistent, and reliable reporting.
• Identify and implement opportunities for automation, advanced analytics, and process improvements.
• Present insights and recommendations, translating data into clear, actionable business strategies.

The Operations Data Scientist transforms complex operational, production, and reliability data into actionable insights that improve decision-making, optimize performance, and support strategic initiatives. This role partners with cross-functional teams to develop predictive models, analytics, and reporting solutions that enhance operational efficiency, reduce risk, and improve asset reliability. The ideal candidate combines strong technical expertise with the ability to translate business challenges into data-driven solutions and effectively communicate insights to both technical and non-technical audiences., * Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, Analytics, or a related STEM field.

• 7+ years of experience in data science, advanced analytics, or a related quantitative field.
• Strong proficiency in Python and SQL for data analysis, modeling, and transformation.
• Experience working with operational, production, maintenance, or time-series data.
• Strong foundation in statistics, applied mathematics, and machine learning methods.
• Experience with cloud-based data platforms (e.g., Snowflake, Databricks, or similar).
• Experience with business intelligence and data visualization tools (e.g., Power BI, Tableau, or Spotfire).
• Strong analytical, problem-solving, and critical thinking skills.
• Ability to work independently while collaborating effectively across technical and business teams.
• Strong communication skills, with the ability to explain complex concepts to non-technical audiences.

Preferred Qualifications:

• Master's degree in Data Science, Statistics, Engineering, Analytics, or a related field.
• Experience in oil and gas, industrial operations, manufacturing, or other asset-intensive environments.
• Familiarity with reliability modeling techniques (e.g., Weibull, Cox proportional hazards, Kaplan-Meier).
• Experience with anomaly detection, predictive maintenance, and asset performance optimization.
• Experience supporting digital transformation initiatives, Operate-by-Exception workflows, or AI-enabled solutions.
• Experience with image analytics, drone inspection analytics, or computer vision