Job Description

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 :

• Bachelor's degree in Computer Science, Data Science, Engineering, Applied Mathematics, or a related field; a graduate degree is preferred but not required with sufficient experience.
• 7+ years of hands-on experience in Data Science, Machine Learning, or Data Engineering, with at least 2+ years in a lead or senior role driving projects from concept to production.
• Strong proficiency in Python for data engineering and machine learning, including experience with common data and ML libraries (for example pandas, NumPy, scikit-learn, PyTorch or TensorFlow, statsmodels).
• Strong SQL skills and experience working with relational databases, time-series databases, or data warehouses for large-scale analytics.
• Proven experience working with time-series data in industrial or operational settings, including feature engineering, resampling, handling missing data, and building time-series forecasting or anomaly detection models.
• Experience integrating and analyzing SCADA data or similar industrial control and telemetry systems in energy, utilities, manufacturing, or related heavy industry.
• Solid understanding of data engineering concepts including ETL/ELT pipelines, batch and streaming architectures, and data modeling for analytical workloads.
• Hands-on experience with cloud platforms (such as AWS, Azure, or GCP) and modern data tooling, for example cloud storage, managed databases, containerization, and workflow orchestration tools.
• Practical exposure to MLOps practices, including deployment of models to production, monitoring model performance in real time, and maintaining models over their lifecycle.
• Familiarity with concepts and techniques related to Digital Twin Analytics, physics-informed models, or asset performance management in an industrial context is a strong plus.
• Strong software engineering fundamentals including version control (Git), code review practices, testing, and documentation.
• Demonstrated ability to lead cross-functional initiatives, prioritize a portfolio of projects, and manage stakeholder expectations in a fast-moving environment.
• Excellent communication skills, with the ability to explain complex technical concepts clearly and to influence decision making among operations leaders, field engineers, and executives.
• Willingness to work on-site in the Pittsburgh, PA area and periodically visit field or plant locations as needed; relocation support is available for exceptional candidates.

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