What are the responsibilities and job description for the Data Science AI/ML Engineer - Intern position at Impac Exploration Services?
Job Description
Job Description: 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:
Job Description: 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
- 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
- Familiarity with cloud platforms (AWS, GCP, Azure)
- Experience with big data technologies (Hadoop, Spark)
- Knowledge of the geosciences, energy, or oil and gas industries
- Opportunity to work on real-world projects with a significant impact
- Fully remote (with opportunity for on-site visits to our offices, labs and field locations)
- Encouraged to take advantage of opportunities for professional development, networking, authoring abstracts and research papers, and engagement with the broader data science community