Lead ​/ Sr Analyst Data Scientist

Posted Thursday, August 20, 2026 at 6:00 AM

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Boardwalk is a limited partnership operating in the midstream portion of the natural gas and natural gas liquids industry, providing transportation and storage services for our customers. Our 14,000 miles of pipeline and storage assets provide diverse market connectivity to producers and end-users who need reliable sources of natural gas for power generation, home heating or petrochemical feed stocks. We have the experience, knowledge, and flexibility to design service offerings and create system enhancements tailored to our customers’ needs throughout the 13 states in which we operate.

As an organization focused on sustainability, we are committed to protecting the environment while delivering this energy source. This commitment is made to our customers, employees, and the communities in which we operate. We incorporate environmental stewardship, safety, and compliance into our day-to-day operations and seek to strengthen and support the communities we serve. Additional information about the company can be found online at  .

We are currently looking for an Lead / Sr Analyst Data Scientist for our Houston, TX office.

The Data Scientist plays a key role in advancing digital innovation by developing data-driven models and insights that support operational efficiency, asset optimization, and strategic decision-making. This role will work closely with data engineers, business stakeholders, and digital leadership to design and deploy machine learning (ML) models, predictive analytics, and advanced visualizations that drive measurable business outcomes.

The ideal candidate combines strong analytical skills with deep technical expertise in cloud-based data science tools, particularly within the Databricks and Amazon Web Services (AWS) ecosystem. This role requires a passion for solving complex problems, a collaborative mindset, and the ability to translate data into actionable insights for a midstream energy environment.

• Design, build, and deploy predictive models and machine learning (ML) algorithms to support asset performance, reliability, and commercial optimization.
• Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling using Python, R, or similar tools.
• Develop time series forecasting, anomaly detection, and classification models for operational and business use cases.
• Apply geospatial and sensor data analytics to support pipeline monitoring, flow optimization, and risk assessment.
• Leverage Databricks and Amazon Web Services (AWS) tools such as Sage Maker, Redshift, Simple Storage Service (S3), Lambda, and Glue for model training, deployment, and data access.
• Collaborate with data engineers to ensure models are integrated into production pipelines and dashboards.
• Use version control (e.g., Git), MLFlow and continuous integration/continuous deployment (CI/CD) practices to manage model lifecycle and reproducibility.

• Partner with operations, engineering, and commercial teams to identify high-impact use cases and translate business needs into analytical solutions.
• Present findings and recommendations through compelling data visualizations and storytelling using tools like Power BI.
• Support the development of self-service analytics and promote data literacy across the organization.
• Document model assumptions, limitations, and performance metrics to ensure transparency and trust.
• Monitor model performance and retrain as needed to maintain accuracy and relevance.
• Implement MLOps practices to support scalable, automated model deployment and monitoring.
• Ensure compliance with data governance, privacy, and ethical AI standards.
• Stay current with industry trends and emerging technologies to continuously improve analytical capabilities.

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, or a related field.
• 3–5+ years of experience in applied data science,…