AI Applications & Data Analytics Lead

Granite Construction Incorporated

Sacramento (CA)

Vor Ort

USD 140.000 - 180.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

Schaffe es an den ATS-Filtern vorbei

Zusammenfassung

Granite Construction seeks an AI Applications & Data Analytics Lead to build and scale AI and data solutions that improve safety, production, cost, and performance across construction materials operations.

This role bridges field operations and technology, turning operational data into actionable intelligence and deploying AI-enabled applications that enhance decision making, automate workflows, and boost productivity.

Qualifikationen

  • Bachelor’s Degree in artificial intelligence, computer science, data science, or related quantitative fields.
  • Equivalent practical experience may be considered for candidates with a strong portfolio.
  • Minimum of two years of professional experience in software development, data analytics, AI or machine learning implementation.

Aufgaben

  • Design, develop, and maintain AI-enabled applications supporting aggregate and asphalt plant operations and sales.
  • Develop ML models to optimize production, pricing, plant uptime, and cost performance.
  • Integrate AI solutions with existing operational software, plant systems, sensors, and databases.
  • Enhance sales efficiency through the use of AI and bots to review specifications.
  • Modernize legacy software systems through automation, predictive analytics, and decision-support tools.

Kenntnisse

AI/ML development
Data analytics
Cloud & data pipelines
SQL / Databases
Software development

Ausbildung

Bachelor's Degree in AI / CS / Data Science

Tools

APIs
Microservices
Databases

Jobbeschreibung

Role Summary

The AI Applications & Data Analytics Lead is responsible for building and scaling practical AI and data solutions that drive measurable improvements in safety, production, cost, and performance across construction materials operations.

This role serves as the bridge between field operations and technology—turning operational data into actionable intelligence and deploying AI-enabled applications that improve decision‑making, automate workflows, and enhance productivity.

The position combines hands‑on development capability with business leadership—owning the identification, prioritization, and execution of AI and analytics use cases across plants, sales, and support functions.

Essential Job Accountabilities

Design, develop, and maintain AI‑enabled applications supporting aggregate and asphalt plant operations and sales. Responsibilities include:

  • Develop and implement machine learning and advanced analytics models to optimize production, pricing, plant uptime, and cost performance.
  • Optimize product pricing with sales data including pricing and GIS data.
  • Integrate AI solutions with existing operational software, plant systems, sensors, and databases.
  • Enhance sales efficiency through the use of AI and bots to review specifications.
  • Modernize legacy software systems through automation, predictive analytics, and decision‑support tools.
  • Build and maintain data pipelines, APIs, and services enabling real‑time insights.
  • Analyze large datasets from production systems, quality labs, and equipment to generate actionable intelligence.
  • Collaborate with operations, sales, quality, maintenance, and IT teams to translate business needs into technical solutions.
  • Ensure AI solutions align with cybersecurity standards, company policies, and responsible AI practices.
  • Document systems, models, and processes to support long‑term sustainability and user adoption.
  • Continuously evaluate emerging AI and software technologies relevant to construction materials.
Education
  • Bachelor’s Degree in artificial intelligence, computer science, data science, mathematics, or related quantitative fields. Equivalent practical experience may be considered for candidates with a strong portfolio.
Work Experience
  • Minimum of two (2) years of professional experience in software development, data analytics, AI or machine learning implementation.
  • Experience delivering production‑ready apps, and software solutions in operational or industrial environments.
  • Strong data analytics skills.
  • Experience working with databases, analytics platforms, and modern application architecture.
Knowledge, Skills, and Abilities
  • Experience in aggregate, asphalt, construction materials, mining, sales or manufacturing operations.
  • Familiarity with plant control systems, sales and production data.
  • Experience with predictive maintenance, quality analytics, or process optimization.
  • Strong communication skills and ability to work with non‑technical stakeholders.
  • Applied artificial intelligence and machine learning.
  • Industrial software development and systems integration.
  • Data‑driven problem solving.
  • Operational and sales mindset focused on safety, quality, and efficiency.
  • Collaborative and partnership mindset.
Physical Demands

The employee must talk and hear regularly, stand, walk, sit and use hands to operate a computer keyboard. Must be able to walk uneven and steep ground to access property for interacting with operations and sales employees. Must meet vision requirements. Reasonable accommodations may be made for qualified employees with disabilities.

Additional Requirements / Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mandatory Mand.
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