Data Science AI ML Engineer - Intern at Impac Exploration Services Inc Weatherford, OK

Downtown Boulder Partnership · Weatherford, Oklahoma, USA

  • Internship

About the role

Role

As an integral part of our organization, you will contribute to the development and implementation of cutting‑edge machine learning and artificial intelligence solutions. You will collaborate with cross‑functional teams across our organization to solve complex problems and drive innovation.

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

Qualifications

  • 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
  • At this time we are not sponsoring visas or participating in CPT programs

Preferred Qualifications

  • 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

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