Part-Time ML Delivery Lead for Energy AI Programs

Bechtel Corporation; Houston (TX); Hybrid; USD 140,000 - 190,000; Part time

Job summary

Bechtel is seeking an ML Delivery Lead in Houston for part-time telework and in-person weekly attendance. You will drive end-to-end ML and predictive analytics delivery across the Energy Business Unit, translating business needs into scalable, governance-backed solutions.

The role requires strong ML, data, and EPC delivery expertise to ensure measurable value. The position combines ML knowledge with digital delivery experience and EPC familiarity to deliver production-ready capabilities from

Qualifications

• Requires bachelor’s degree and 10–13 years of relevant experience or 14–17 years in energy/industrial environments.
• Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, or related field. Master’s degree preferred.

Responsibilities

• Lead end-to-end delivery of ML and predictive analytics solutions.
• Oversee the ML lifecycle from development through deployment and governance.
• Ensure data availability, quality, and proper feature engineering for ML models.
• Define business outcomes, success metrics, and value targets; coordinate with Change Management.

Skills

Delivery management

Stakeholder leadership

ML lifecycle

Data preparation

Feature engineering

MLOps

Azure

Program management

Communication

Education

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

Master’s degree in a related technical field preferred

Tools

Azure

Job description

Bechtel is seeking an ML Delivery Lead in Houston for part-time telework and in-person weekly attendance. You will drive end-to-end ML and predictive analytics delivery across the Energy Business Unit, translating business needs into scalable, governance-backed solutions.

The role requires strong ML, data, and EPC delivery expertise to ensure measurable value. The position combines ML knowledge with digital delivery experience and EPC familiarity to deliver production-ready capabilities from