Data Science Intern

Location of Work
Edmonton, AB and ability to work remote

Qualifications
Pursuing an undergrad or grad program in a quantitative field: mathematical sciences, computing science, statistics, or another. If your field of study differs, apply we’d like to hear why you'll be a good fit regardless.

A day in the life of a Data Science Intern...

1. You will be working on the Data and Analytics team, to empower business partners by providing powerful tools and insightful analytics.
2. You will work in an enriching environment, where you are provided the necessary tools, resources, and encouragement to learn something new every day.
3. You need to have the willingness to get your hands dirty with maintaining ML models and understanding data.
4. You will learn about real-world applications of ML, from the development of a model all the way through to deploying it in a real-world scenario.
5. You will manage your time effectively by respecting deadlines, updating your manager on progress, and communicating effectively with staff/stakeholders.
6. The ability to communicate insights from ML models in an understandable format to non-experts will be key.
7. The opportunity to closely watch and be part of the implementation of Gen AI products/services.
8. Mentors are readily available to provide guidance, answer questions, and help you overcome challenges.

Required Skills

1. Basic knowledge of R and Python.
2. Basic knowledge of Statistics: Probability and Statistics, Regression Analysis and Machine Learning.
3. Plus: Deep Learning and Natural Language Processing.
4. Working knowledge of using AWS services is a plus.
5. Familiarity with Python libraries such as Pandas, Matplotlib, Numpy, Scikit Learn.
6. Good to have familiarity with LLMs but not a must.

Description Of The Role And Responsibilities

1. Understand and implement data science procedures and algorithms to solve business problems with a cross-functional team.
2. Be involved in technical and non-technical aspects of data science and analytics projects.
3. Analyze and present results of data mining processes to the stakeholders.
4. Collaborate with data scientists, data engineers, and business analysts to understand the requirements and create models that address the business problems or create new opportunities for the business.
5. Brainstorm solutions and algorithms opportunities to all our business challenges.
6. Take initiative to tackle and solve problems outside of scope and package solutions as reusable, modular components.
7. Prototype, develop and deploy models into production using state-of-the-art technologies. Model validation and optimization is a crucial aspect of a production system that you will also be responsible for.
8. Communicate and collaborate to build a team-oriented learning environment with team members.
9. Learn by doing, being proactive, and asking questions.
10. Write quality code to build quality products.

Necessary Competencies

1. Superior strategic thinking and planning.
2. Ability to nurture and contribute to innovation and creative solutions.
3. Strong analytical and excellent communication skills; ability to organize and analyze data and present summaries of findings.
4. Strong verbal and written communicator to a wide breadth of audiences including senior management.
5. Independent thinker capable of self-starting and problem-solving skills is a must.
6. Self-motivated to learn and excel.
7. Ability to ask for help.

Work Description
Example projects include:

1. A prediction model which will generate the predicted time of arrival for the roadside assistance unit based on the location of the car and the time when the car broke down.
2. A membership coverage recommendation model which will suggest the right plan to a customer based on a set of customer characteristics and a set of feature characteristics of all plans.
3. A segmentation model which will extract the segments of customers based on customer characteristics, lifetime value of a member, and some feature characteristics of a plan.

Expected Outcome From Students

1. Model Artifacts/Code
2. Preparing data visualizations using industry-leading tools to summarize results for stakeholders.
3. Documentation of your results - tell the story of the work, the successes, and the lessons-learned for posterity.
4. Business value/impact/outcomes, etc.
5. Recommendations for next phase if any.

Benefits To Students

1. Convert academic knowledge into industry skills.
2. Build a portfolio of projects/data analyses.
3. Build a professional network with future colleagues.
4. Become a better problem solver and communicator.
5. Gain practical experience measuring the effectiveness of different solutions.
6. Learn how corporations use data science to make their business more efficient.
7. Gain experience working with mathematical models and statistical analysis tools such as Python.
8. Gain experience applying Data Science to real-world problems.
9. Gain practical experience to decide if the occupation is the right one or not.

Work Model
Remote

We thank all applicants for their interest; however, only those selected for an interview will be contacted.