Lead Machine Learning Engineer

Job Title : Machine Learning Lead Engineer

Work Location : Pittsburgh, PA

Duration : 6 month Contract-to-Hire

Education / Experience Required : Leading ML / DS projects in an industrial setting, experience working with time-series data and SCADA applications.

Job Description & Responsibilities :

• Lead end-to-end delivery of data engineering and machine learning projects that improve operational efficiency and reduce emissions in natural gas development, production, and midstream operations.
• Own the full lifecycle of ML solutions from ideation and scoping, to data discovery, modeling, validation, deployment, and long-term monitoring.
• Provide technical leadership for a small but high-impact Data Engineering & Machine Learning team, including mentoring, code reviews, and setting best practices for quality and reliability.
• Work on-site with engineering, operations, and SCADA teams on-site to understand real-world constraints, define use cases, and translate business problems into data-driven solutions.
• Lead the effort to productionize existing models and scale multiple proof-of-concept solutions into robust, maintainable, and observable production systems.
• Architect, design, and implement data pipelines that ingest, clean, transform, and store large volumes of time-series and event data from SCADA systems, field sensors, and other industrial data sources.
• Develop and refine machine learning models focused on time-series problems such as forecasting, anomaly detection, remaining useful life estimation, and performance optimization of assets.
• Contribute to and help lead Digital Twin Analytics initiatives, integrating physics-based and data-driven models to simulate and optimize complex systems in the field.
• Collaborate on the design and implementation of anomaly detection frameworks that surface early warning signals for equipment failure, process instability, and emissions events.
• Partner with software and DevOps engineering to design and implement MLOps practices, including CI / CD for ML, feature stores, model versioning, model performance tracking, and automated retraining pipelines.
• Establish standards for model validation, A / B testing, and offline / online evaluation to ensure robustness and reliability in operational environments.
• Communicate technical concepts, tradeoffs, and results clearly to both technical stakeholders and non-technical operations leaders, including presenting insights and recommendations to leadership.
• Evaluate new tools, libraries, and cloud-native services in the data and ML ecosystem and make recommendations to improve performance, scalability, and developer productivity.
• Champion a culture of experimentation, data quality, and continuous improvement while keeping a strong focus on safety, regulatory compliance, and environmental impact.

Skills & Qualifications :