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Business Data & AI Specialist – GTM Data & Systems Strategy

Job in Houston, Harris County, Texas, 77246, USA
Listing for: Raise
Full Time position
Listed on 2026-09-16
Job specializations:
  • IT/Tech
    Data Analyst, AI Engineer (Applied/Software), Data Scientist, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 49 - 52 USD Hourly USD 49.00 52.00 HOUR
Job Description & How to Apply Below

Business Data & AI Specialist – GTM Data & Systems Strategy

  • Pay Rate: $49.00 - $52.00/hour (on W2)
  • Contract Length: 1 Year
  • Location: Houston, TX 77079
  • Work Type: Hybrid - Remote on Wed & Fri

Raise is currently hiring a contract team member on behalf of our client. They’re expanding their team to meet growing needs, making this a unique opportunity to work with an industry leader.

Overview

GTM D&SS is seeking a Business Data & AI Specialist to work within the Gas Transmission business and deliver applied data science, AI/ML, and automation solutions that directly support operational priorities including reliability, compliance, asset management, and decision support.

The role will be embedded within D&SS and will partner closely with operations, engineering, integrity, records, asset management teams, etc. to understand their challenges, define requirements, and translate them into practical, data-driven solutions. The role requires strong technical execution skills with a focus on understanding the business context, delivering measurable outcomes, and ensuring solutions are usable, auditable, and aligned with how the business operates.

This role leverages technology platforms to build and deliver business-owned solutions that address GTM-specific needs.

Responsibilities
  • Partner with business stakeholders across operations, engineering, reliability, records, and asset management to identify, scope, and prioritize data and AI opportunities. Translate business questions and operational pain points into well-defined analytical or modeling problems. Ensure solutions are grounded in business context, regulatory requirements, and operational realities.
  • Perform data acquisition, cleansing, transformation, and validation across structured and unstructured datasets. Conduct exploratory analysis to surface trends, anomalies, risks, and improvement opportunities relevant to GTM operations.
  • Design, build, test, and tune machine learning models using established techniques (e.g., classification, regression, clustering, natural language processing) to address specific business use cases.
  • Build Generative AI and Agentic AI-based solutions, including prompt engineering and workflow automation.
  • Deliver reproducible analyses and clearly communicate findings, recommendations, and limitations to both technical and non-technical audiences.
  • Create business-facing visualizations and dashboards that support day-to-day decision-making.
  • Prepare and maintain documentation that supports knowledge transfer, auditability, and operational continuity.
  • Apply appropriate evaluation methodologies and document assumptions, limitations, and model performance.
  • Write clean, well-structured Python code that meets quality and security standards, working within shared repositories (Git).
  • Support model deployment and operationalization, including basic MLOps practices such as monitoring inputs, outputs, and performance over time.
  • Collaborate with D&SS, TIS, business partners, and domain experts to ensure solutions meet operational needs.
  • Identify opportunities to enhance or extend existing business solutions within the assigned domain.
  • Stay current on practical advances in data science, ML, and AI that are relevant to the business context.
Required Qualifications
  • Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related field.
  • 6-8 years of combined experience applying data, analytics, and AI/ML to business or operational problems, with demonstrated ability to translate business needs into practical, data-driven solutions in the energy industry.
  • Strong proficiency in Python and common data science libraries.
  • Solid understanding of applied machine learning concepts and applied statistics.
  • Demonstrated…
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