Advisor – Data Trust & AI Products

Occidental Petroleum•Houston, TX

About The Position

Oxy produces, markets, and transports oil and natural gas to maximize value and provide resources fundamental to life. The company leverages its global leadership in carbon management to advance lower-carbon technologies and products. Headquartered in Houston, Oxy primarily operates in the United States, Middle East and North Africa. Oxy strives to attract and retain talented employees by investing in their professional development and providing rewarding opportunities for personal growth. Our goal is to meet the highest employer standards by ensuring the health and safety of our employees, protecting the environment, and positively impacting our communities where we do business. Are you passionate about building AI-powered products that transform how people work while ensuring the data behind them is trusted, protected, and business-ready? Oxy is seeking a unique technical leader who thrives at the intersection of Data Quality, Data Governance, AI Development, and Product Innovation. This highly visible role will spend approximately 50% of its time driving enterprise data quality and data trust initiatives and 50% building AI-powered products, copilots, intelligent agents, and data-driven solutions that create measurable business value. You will play a critical role in shaping Oxy's AI future by ensuring our data is trusted, protected, and AI-ready while simultaneously developing innovative products that leverage that foundation. This role offers a rare opportunity to influence enterprise data strategy, build cutting-edge AI solutions, and drive transformation across the organization.

Requirements

10+ years of experience in Data Management, Data Engineering, Data Governance, AI Development, or Product Engineering.
Strong expertise in SQL, Python, and cloud-native development.
Hands-on experience with Databricks & AWS.
Experience developing AI solutions using LLMs, RAG, vector databases, AI agents, or enterprise AI platforms.
Strong understanding of enterprise data quality, governance, metadata, and data protection.
Experience delivering products from concept through deployment and adoption.
Ability to work directly with business stakeholders to identify opportunities and rapidly build solutions.
Experience with CI/CD pipelines, Azure DevOps, GitHub, or equivalent development platforms.
Experience deploying AI applications in cloud environments including AWS and Azure.
Understanding of MLOps practices including model lifecycle management, monitoring, observability, and performance optimization.
Experience implementing logging, monitoring, telemetry, and usage analytics for AI applications.
Knowledge of security, authentication, authorization, and enterprise architecture principles.

Nice To Haves

Experience building enterprise copilots, knowledge assistants, or conversational AI solutions.
Experience with Microsoft Copilot Studio, Power Platform, and enterprise automation technologies.
Experience building AI solutions using enterprise data platforms such as Databricks.
Experience leveraging enterprise data catalogs, metadata, lineage, and governance capabilities within AI solutions.
Experience working with structured and unstructured data at enterprise scale.

Responsibilities

Establish and lead enterprise data quality measurement across structured data domains.
Define data quality KPIs, scorecards, and executive dashboards.
Implement automated monitoring and controls for completeness, accuracy, consistency, and timeliness.
Drive enterprise data classification, protection, and governance initiatives.
Partner with business leaders and data owners to improve data quality and accountability.
Enable trusted, governed, and AI-ready data assets across the enterprise.
Support metadata, lineage, stewardship, and data catalog initiatives.
Design, build, and deploy AI-powered applications, copilots, intelligent agents, and business solutions.
Partner directly with business teams to identify opportunities where AI can drive measurable value.
Develop GenAI solutions leveraging LLMs, RAG architectures, enterprise knowledge bases, and agentic workflows.
Build prototypes, MVPs, and production-ready AI products that solve real business problems.
Integrate enterprise data sources into AI applications while ensuring governance and security requirements are met.
Evaluate emerging AI technologies and drive innovation across the enterprise.
Translate business challenges into scalable AI products and solutions.