Chevron-posted 12 months ago
$47,840 - $95,680/Yr
Full-time • Intern
Houston, TX
Petroleum and Coal Products Manufacturing

Chevron is seeking interns in the Information and Analytics job family within Information Technology to support the global energy transition towards lower carbon solutions. The role involves applying data science and machine learning expertise to create data products that deliver significant business value. Interns will receive mentorship and participate in professional development activities while working in a collaborative environment focused on innovation and technology.

  • Identify and frame opportunities to apply advanced analytics, modeling, and related technologies to data that provide insight and improve decision making, and automation
  • Identify data necessary and appropriate technology to solve business challenges
  • Clean data, develop models, and test models
  • Establish the life cycle management process for models
  • Provide technical mentoring in modeling and analytics technologies, the specifics of the modeling process, and general consulting skills
  • Identify, acquire, cleanse/prepare, store data, and develop reusable data products aligned with defined architecture patterns
  • Create and manage data pipelines that enable advanced analytics models, and handle data challenges and opportunities
  • Ensure the scalability and reliability of model deployment, and document the technical aspects of the process
  • Develop and share reusable tools for data engineering tasks, and leverage technical services to optimize data workflows
  • Consult, identify and frame opportunities to implement AI solutions that help gain insight and improve decision making and automation
  • Identify data, technology, and architectural design patterns to solve business challenges using analytical tools and AI design patterns and architectures
  • Partner with Data Scientists and Chevron IT Foundational services to implement complex algorithms and models into enterprise scale machine learning pipelines
  • Build machine and deep learning systems optimized for scalability and performance
  • Transform data science prototypes into scalable solutions in a production environment
  • 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
  • Run machine learning experiments and fine-tune algorithms to ensure optimal performance
  • Access, gather, and analyze data from source systems
  • Help frame the business problem by providing quantitative and qualitative data analysis (data quality, availability, etc.)
  • Drive insights to business problems by visualizing the data and telling a story through data (report patterns, trends, anomalies, etc.)
  • Participate in the end-to-end product development lifecycle as a member of agile team
  • Contribute to data analysis, data wrangling, data visualization, and acceptance testing
  • Present findings and new development to help refine backlog items
  • Understand the business use of data and stakeholder requirements to support strategic business objectives
  • Collaborate with delivery teams to provide data management direction and support for initiatives and product development
  • Contribute to the design of common information models
  • Consult on the appropriate data integration patterns, data modeling and data quality
  • Maintain and share knowledge of requirements, key data types and data definitions, data stores, and data creation process
  • Currently enrolled in bachelor's or master's degree program in Computer Science, Computer Engineering, Mathematics, Statistics, Operations Research, Data Science, Management Information Systems, or related Engineering 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: Software Engineering background. Working knowledge of mathematics (primarily linear algebra, probability, statistics), and algorithms. Working knowledge of machine learning frameworks and machine learning libraries.
  • 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
  • Experience designing custom APIs for machine learning models for training and inference processes
  • Software engineering skills and fundamentals: coding (Python, R) and Github, source control versioning, requirement spec, architecture, and design review, testing methodologies, CI/CD, etc.
  • Competitive compensation and benefits programs including variable pay, health care coverage, retirement plan, protection coverage, time off and leave programs, training and development opportunities.
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