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Houston, Texas, United States of America

Internship

Onsite

$23.65/hr - $37.50/hr

Intern

Chevron believes in a lower carbon future and is seeking interns to thrive in a digital environment to support the global energy transition. Interns will engage in Information and Analytics, transforming data into insights and managing information architectures to support strategic business objectives.

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Responsibilities

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

Qualification

Required

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

Preferred

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