Senior Data Science, AI & Analytics Engineer

Clean Energy•, TX

1d•$140,000 - $150,000

About The Position

Clean Energy Fuels is North America's largest provider of renewable natural gas (RNG) and alternative fueling solutions for the transportation industry. We operate a nationwide network of fueling infrastructure that supports fleets across transit, trucking, municipal services, airports, waste management, and other critical industries. At Clean Energy Fuels, we are investing in next-generation analytics, AI, Industrial IoT, cloud platforms, and intelligent automation to improve operational performance, increase reliability, enhance decision-making, and support our mission of delivering cleaner transportation energy solutions. This is not a traditional reporting or analytics role. It is an opportunity to build practical AI solutions, intelligent automation capabilities, modern data products, and operational intelligence platforms that support real-world business operations, connected assets, field teams, and enterprise decision-makers across North America. We offer a competitive compensation and benefits package with compensation between $140,000 -$150,000 for well qualified candidates. As part of the IoT, Data Science & Advanced Analytics team, you will work on initiatives that directly impact operations, supply chain, finance, environmental reporting, asset performance, predictive maintenance, and business productivity. At Clean Energy, your work won't disappear into another advertising algorithm or consumer application. You'll have the opportunity to solve meaningful business challenges using AI, analytics, automation, and IoT technologies that directly impact real-world operations, sustainability initiatives, and critical infrastructure across North America. As part of the IoT, Data Science & Advanced Analytics team, you'll work at the intersection of: Artificial Intelligence and Intelligent Automation, Industrial IoT and Operational Intelligence, Advanced Analytics and Data Science, Cloud Data Platforms and Modern Data Engineering, Predictive Maintenance, Digital Twin and Asset Performance Management, Enterprise Reporting and Business Intelligence, Digital Transformation and Process Automation. You will help build solutions that support hundreds of connected assets, operational facilities, business processes, and decision-makers across the organization. This role offers the opportunity to work with modern technologies including Azure Databricks, Generative AI, AI Agents, Power BI, Grafana, cloud analytics platforms, enterprise and third-party APIs, IoT telemetry, streaming data, and intelligent automation capabilities while partnering directly with business leaders and operational teams. If you enjoy solving complex problems, building practical AI solutions, modernizing business processes, and creating technology that delivers measurable business value, you'll find a unique opportunity to make a significant impact at Clean Energy. Reporting to the Director, IoT, Data Science & Advanced Analytics, the Senior Data Science, AI & Analytics Engineer is a hands-on technical role responsible for designing, developing, and supporting artificial intelligence, intelligent automation, data engineering, analytics, and operational intelligence solutions across Clean Energy Fuels. The successful candidate will work across the full solution lifecycle—from business requirements and architecture through development, deployment, and support—to deliver scalable solutions that improve operational visibility, automate business processes, enhance reporting capabilities, and enable data-driven decision making. This role combines data engineering, AI enablement, cloud technologies, enterprise integrations, operational intelligence, and IoT analytics to solve complex business challenges across multiple functions of the organization.

Requirements

7–10+ years experience
Enterprise-scale data platforms
Cloud analytics architecture
API integration development
Advanced SQL and Python

Nice To Haves

Power BI administration and governance experience.
PySpark and distributed processing experience.
Data governance and data quality experience.
Experience supporting AI and machine learning solutions.
IoT, telemetry, or operational analytics experience.
Experience with enterprise data modernization initiatives.

Responsibilities

Design, develop, and maintain scalable data pipelines, enterprise data products, APIs, integrations, and analytics solutions using Azure Databricks, SQL, Python, and cloud-based technologies.
Develop and support data integration frameworks connecting ERP systems, operational platforms, financial applications, IoT devices, external systems, and third-party data providers.
Design and maintain secure API-based integrations that enable efficient data exchange across enterprise applications and business processes.
Develop trusted, reusable, and governed datasets that support reporting, analytics, AI, automation, and operational intelligence initiatives.
Implement modern data engineering practices including data quality validation, automated testing, monitoring, observability, lineage tracking, and performance optimization.
Improve scalability, reliability, maintainability, and security of enterprise data and analytics platforms.
Modernize manual, spreadsheet-based, and legacy business processes through automation and cloud-based solutions.
Design, develop, and support AI-enabled solutions that improve productivity, operational efficiency, and business decision-making.
Build intelligent automation workflows using Generative AI, machine learning, AI agents, APIs, workflow orchestration, and enterprise data platforms.
Develop AI-powered assistants, custom copilots, recommendation engines, knowledge solutions, and decision-support capabilities.
Integrate AI services and intelligent automation into enterprise applications, reporting solutions, and operational workflows.
Develop predictive analytics, intelligent auditing, anomaly detection, forecasting, optimization, and recommendation solutions.
Identify opportunities to reduce manual effort through automation and AI-driven process transformation.
Collaborate with business stakeholders to evaluate, prioritize, and implement high-value AI initiatives.
Support AI governance, monitoring, testing, and continuous improvement to ensure scalable and responsible AI adoption.
Design and support data pipelines and analytics solutions that leverage IoT, telemetry, operational, and industrial data sources.
Develop near real-time operational monitoring, alerting, and intelligence capabilities that improve visibility into business and field operations.
Support predictive maintenance, condition-based monitoring, asset performance management, and operational optimization initiatives.
Build scalable streaming and event-driven data solutions capable of processing high-volume operational and telemetry data.
Develop dashboards, analytics, and operational intelligence solutions that provide visibility into asset health, utilization, reliability, performance trends, and business KPIs.
Collaborate with Operations, Engineering, and business teams to identify opportunities to improve efficiency, reduce downtime, and optimize performance through data, AI, and automation.
Support the evolution of connected asset, digital operations, and industrial intelligence capabilities across Clean Energy's fueling and operational network.
Support enterprise data governance, reporting certification, and data quality initiatives.
Develop automated monitoring, validation, reconciliation, and exception-reporting capabilities.
Assist with metadata management, lineage tracking, documentation, and governance practices.
Partner with business stakeholders to improve the accuracy, consistency, reliability, and trustworthiness of enterprise data assets.
Power BI development.
Semantic model and dataset development.
DAX and Power Query.
Dashboard design and optimization.
Reporting automation and self-service analytics.
Translate business requirements into practical and scalable technical solutions.
Participate in all phases of solution delivery including discovery, design, development, testing, deployment, and production support.
Collaborate closely with cross-functional teams to ensure solutions align with business objectives.
Contribute to technical documentation, knowledge sharing, and continuous improvement initiatives.