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Impac Exploration Services · 5 months ago

Data Science AI/ML Engineer - Intern

Houston, TX, US

Internship

Onsite

Intern

Impac Exploration Services is seeking an intern to contribute to the development and implementation of machine learning and artificial intelligence solutions. The role involves collaborating with cross-functional teams to solve complex problems and drive innovation through data analysis and model deployment.

EnergyOil and Gas

No H1B

Responsibilities

Conduct exploratory data analysis to uncover insights and trends

Develop and implement machine learning models using Python and relevant libraries

Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions

Participate in the deployment of models into production environments

Stay up-to-date with the latest advancements in data science and machine learning

Properly document code, track and monitor experiments, and build necessary repositories

Qualification

Machine LearningPythonData Analysis LibrariesData StructuresCloud PlatformsBig Data TechnologiesCI/CD WorkflowsGeosciences KnowledgeProblem-SolvingCommunication SkillsInterpersonal Skills

Required

Pursuing a Master's or PhD degree in Computer Science, Statistics, Mathematics, or a related field. In lieu of a degree, a proven track record of innovative projects or research in the field of ML/AI

Strong foundation in data structures, algorithms, and statistics

Proficiency in Python programming and data analysis libraries

Experience with machine learning frameworks and libraries

Excellent problem-solving and analytical skills

Strong communication and interpersonal skills

Preferred

Familiarity with cloud platforms (AWS, GCP, Azure)

Experience with big data technologies (Hadoop, Spark)

Understanding of good CI/CD workflows and processes

Knowledge of the geosciences, energy, or oil and gas industries

Benefits

Opportunity to work on real-world projects with a significant impact

Encouraged to take advantage of opportunities for professional development, networking, authoring abstracts and research papers, and engagement with the broader data science community