Chevron-posted 6 days ago
$94,000 - $110,000/Yr
Full-time • Entry Level
Houston, LA
5,001-10,000 employees

Chevron believes the future of energy is lower carbon. And we know that affordable, reliable, ever-cleaner energy is essential to achieving a more prosperous and sustainable world. For Chevron, reducing the carbon intensity of oil and gas represents a tremendous opportunity to advance the global net zero ambitions of the Paris Agreement and build a lower-carbon economy. Technology will play a crucial role in achieving this goal, and Chevron is seeking professionals with a desire to thrive in a cutting-edge digital, global environment to securely advance the global energy transition. We differentiate ourselves through the application of technology, taking an approach that includes in-house expertise, proprietary solutions, and strategic partnerships. Investing in people is a key component of our company – you will be supported by mentoring programs and employee networks to excel your professional development. Rewards include competitive pay, cash incentives, flexible benefit programs and flexible work schedules – every other Friday off and remote work where approved. Information and Analytics is one of five job families within Information Technology. Roles within this job family understand the use of data, insights, and information to support work processes and strategic business objectives: Transforms data into insights for making better decisions across the enterprise. Develops, executes, and manages information and data architectures, governance, practices, and procedures. Manages the full information and data lifecycle needs of the enterprise.

  • Data Scientist: Identify and frame opportunities to apply advanced analytics, modeling, and related technologies to data that provide insight and improve decision making, and automation
  • Data Scientist: Identify data necessary and appropriate technology to solve business challenges
  • Data Scientist: Clean data, develop models, and test models
  • Data Scientist: Establish the life cycle management process for models
  • Data Scientist: Provide technical mentoring in modeling and analytics technologies, the specifics of the modeling process, and general consulting skills
  • Data Engineer: Identify, acquire, cleanse/prepare, store data, and develop reusable data products aligned with defined architecture patterns
  • Data Engineer: Create and manage data pipelines that enable advanced analytics models, and handle data challenges and opportunities
  • Data Engineer: Ensure the scalability and reliability of model deployment, and document the technical aspects of the process
  • Data Engineer: Develop and share reusable tools for data engineering tasks, and leverage technical services to optimize data workflows
  • Machine Learning Engineer: Consult, identify and frame opportunities to implement AI solutions that help gain insight and improve decision making and automation
  • Machine Learning Engineer: Identify data, technology, and architectural design patterns to solve business challenges using analytical tools and AI design patterns and architectures
  • Machine Learning Engineer: Partner with Data Scientists and Chevron IT Foundational services to implement complex algorithms and models into enterprise scale machine learning pipelines
  • Machine Learning Engineer: Build machine and deep learning systems optimized for scalability and performance
  • Machine Learning Engineer: Transform data science prototypes into scalable solutions in a production environment
  • Machine Learning Engineer: Orchestrate and configure infrastructure that assists Data Scientists and analysts in building low latency, scalable and resilient machine learning, and optimization workloads into an enterprise software product
  • Machine Learning Engineer: Run machine learning experiments and fine-tune algorithms to ensure optimal performance
  • Business Intelligence Analyst: Access, gather, and analyze data from source systems
  • Business Intelligence Analyst: Help frame the business problem by providing quantitative and qualitative data analysis (data quality, availability, etc.)
  • Business Intelligence Analyst: Drive insights to business problems by visualizing the data and telling a story through data (report patterns, trends, anomalies, etc.)
  • Business Intelligence Analyst: Participate in the end-to-end product development lifecycle as a member of agile team
  • Business Intelligence Analyst: Contribute to data analysis, data wrangling, data visualization, and acceptance testing
  • Business Intelligence Analyst: Present findings and new development to help refine backlog items
  • Data Analyst: Understand the business use of data and stakeholder requirements to support strategic business objectives
  • Data Analyst: Collaborate with delivery teams to provide data management direction and support for initiatives and product development
  • Data Analyst: Contribute to the design of common information models
  • Data Analyst: Consult on the appropriate data integration patterns, data modeling and data quality
  • Data Analyst: Maintain and share knowledge of requirements, key data types and data definitions, data stores, and data creation process
  • Preferred education / degrees Bachelor’s or master’s degree in Computer Science, Mathematics, Statistics, Operations Research, Data Science, Management Information Systems, or related Engineering degree
  • Must be currently enrolled in a four-year college or university and classified as a senior or graduate student with anticipation of receiving a bachelor’s or master’s degree by July 2025; OR college graduates with less than two years’ experience since receiving a degree.
  • Must provide a current, unofficial transcript with online resume (as proof of good academic standing) when applying for this position to be considered.
  • Data acquisition, analysis, modeling, movement, transformation, and preparation experience
  • Demonstrated depth in advanced analytics / data science technologies (e.g., machine learning, operations research, statistics, data mining)
  • Data Analyst: Experience with data modeling, data management, data quality, SQL
  • Data Engineer: Experience using data pipelines, Data Lake and storage configuration, Python, RDBMS & SQL
  • Machine Learning Engineer: Experience developing cloud first solutions using Microsoft Azure Services (Azure Functions, Azure App Services, Azure Event hubs, Azure SQL DB, Azure Synapse etc.)
  • Machine Learning Engineer: Experience implementing machine learning frameworks and libraries (e.g. ML Flow, Kubeflow, Tensorflow. Keras scikit-learn, PyTorch, NumPy, SciPy, etc.)
  • Ability to communicate in a clear and concise manner both orally and in writing.
  • Knowledge of enterprise SaaS complexities including security/access control, scalability, high availability, concurrency, online diagnoses, deployment, upgrade/migration, internationalization, and production support
  • Software engineering skills and fundamentals: coding (Python, R) and Github, source control versioning, requirement spec, architecture, and design review, testing methodologies, CI/CD, etc.
  • To be considered for this position, applicants must be legally authorized to work in the United States as a U.S. citizen or national, asylee, refugee, or lawful permanent resident.
  • Experience designing custom APIs for machine learning models for training and inference processes
  • competitive pay
  • cash incentives
  • flexible benefit programs
  • flexible work schedules – every other Friday off and remote work where approved
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