Description We are looking for a Data Scientist to support advanced analytics and machine learning initiatives in Sacramento, California. This Long-term Contract position focuses on creating practical, production-ready solutions that improve operational decision-making, with an early emphasis on predictive maintenance and fleet performance in asset-heavy environments. The role works closely with business leaders and technical teams to turn complex data into reliable models, useful insights, and scalable AI capabilities.

Responsibilities:

• Build, test, and implement machine learning and analytical models using operational, maintenance, and telemetry data to improve equipment reliability and reduce unexpected downtime.
• Create pilot solutions with measurable success criteria, then develop high-performing concepts into stable model pipelines suitable for ongoing production use.
• Convert analytical results into clear recommendations, dashboards, or decision-support outputs that help both technical teams and business stakeholders act with confidence.
• Partner with data engineering teams to shape requirements for data intake, transformation, feature creation, and model delivery across the broader data environment.
• Document methodologies, assumptions, dependencies, and performance results to support transparency, repeatability, and effective model governance.
• Provide input on enterprise data architecture needs from a data science perspective, including dataset design, feature availability, experiment tracking, and model readiness.
• Identify data limitations, quality concerns, and enrichment opportunities that could influence model accuracy or business value.
• Work with cross-functional partners to define new analytics and AI opportunities and assess external tools or vendor-developed solutions when needed. Requirements
• Master's degree or PhD in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, or another closely related quantitative discipline.
• At least 5 years of practical experience developing and deploying predictive analytics or machine learning solutions in business settings.
• Strong proficiency in Python or R, along with hands-on use of core data science libraries such as pandas, scikit-learn, XGBoost, or LightGBM.
• Experience with large-scale data environments and cloud-based platforms such as Azure, Databricks, or similar technologies.
• Solid understanding of model evaluation, validation techniques, feature engineering, and methods for preventing data leakage.
• Background working with technologies such as Apache Spark, Apache Hadoop, Apache Kafka, and ETL processes is highly desirable.
• Ability to communicate technical findings clearly to varied audiences and translate business needs into effective analytical approaches.
• Experience in construction, mining, transportation, manufacturing, energy, or other asset-intensive industries is preferred.

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