Precision Drilling is launching our 2023 Internship Program!

If you are a current post-secondary student working towards your degree in Computer Science or a related field and you’d like to gain real-world experience during your summer, then please read on…

We’re looking for a Data Science Intern to join our team for summer.

Working for Precision Drilling means being immersed in a supportive culture that recognizes you as a strategic player in Precision Drilling’s future. We are a company rooted in the success of our people, where you will collaborate with leadership and your colleagues across the organization.

Our Interns will enjoy perks such as:

• A work environment where you can expect to enjoy a work-life balance that promotes personal health and well-being.
• Personal development to grow your career with us based on your strengths and interests.
• Networking and employee engagement events.
• Career development seminars.
• Precision offers afternoons off before a long weekend.

How to Apply

Applicants MUST submit a Cover Letter detailing their interest in working at Precision Drilling. Failure to submit a Cover Letter will result in automatic disqualification. Please submit your Cover Letter & Resume in ONE PDF file attached in the resume section.

This application will close on Wednesday, March 8.

The following restrictions apply to all internship candidates:

• Applicants must be available for a non-negotiable start date of Monday, May 8, 2023.
• Applicants must be 18 years or older.
• Applicants must have housing accommodations or transportation in the Nisku area, as Precision does not provide internship housing.
• By May 8, 2023, applicants must have completed at least their first year of study in a post-secondary program/Technical Institute.
  

Summary

The Data Science Intern for Field training and Development will gather data and report to managers on effectiveness of training programs.

Responsibilities

• Conduct a learning impact study over 100 hours to assess the effectiveness of training programs.
• Gather, clean, and analyze data from various sources, including surveys, assessments, and learning management systems.
• Develop visualizations and presentations to communicate results to stakeholders.
• Collaborate with trainers and training managers to identify areas for improvement and recommend changes to training programs.
• Perform statistical analysis to identify trends, patterns, and correlations in the data.
• Create reports, dashboards, and other data products to share findings and insights with stakeholders.
• Stay up to date with industry best practices and advancements in data science and training evaluation methods.
• Contribute to the ongoing development and maintenance of the training department's data science capabilities.
• Adhere to data privacy and security protocols to protect confidential information.

Knowledge & Skills

Data analysis: ability to gather, clean, and analyze data from multiple sources.
Statistical analysis: knowledge of statistical methods for identifying trends, patterns, and correlations in data.
Data visualization: ability to create meaningful and effective visualizations to communicate results.
Programming: proficiency in data analysis tools, such as R or Python.
Communication: excellent verbal and written communication skills, with the ability to present findings and insights to stakeholders.
Interpersonal skills: ability to collaborate with trainers, training managers, and other stakeholders.
Problem-solving: strong analytical and problem-solving skills.
Attention to detail: meticulous attention to detail and accuracy when working with data.
Data privacy and security: understanding of data privacy and security protocols, and commitment to protecting confidential information.
Adaptability: ability to adapt to changing requirements and work on multiple projects simultaneously.
Time management: effective time management skills, with the ability to prioritize tasks and meet deadlines.
Continuous learning: commitment to ongoing learning and development in data science and training evaluation methods.
Knowledge of data visualization techniques and tools, such as Tableau, Power BI, and Excel dashboards.

Education

Pursuing a degree in computer science, mathematics, statistics, or a related field.

Experience

Experience with data analysis tools, such as R or Python.

Prior experience with data-driven projects or research is a plus, but not required.