Data Science Intern

About trophi.ai

trophi.ai builds AI-powered performance coaching tools that help people improve their skills faster
through data-driven feedback. We utilize gameplay/telemetry data to generate actionable
insights, and we continually experiment with new approaches across multiple competitive games
and simulation environments.

The Role 

We’re looking for a Data Science Intern to join our team and help turn real-world data into models,
metrics, and insights. You’ll work closely with our team on practical, end-to-end data science
tasks, ranging from dataset preparation and analysis to model training, evaluation, and lightweight
tooling that supports research and experimentation.
This role is a strong fit if you enjoy hands-on problem-solving, working with real data, and iterating
to create ideas.

Key Responsibilities 

• Clean, prepare, and validate datasets from multiple sources (structured and semistructured).
• Perform exploratory data analysis (EDA) to identify patterns, anomalies, and opportunities
  for modeling.
• Build and improve pipelines for data preprocessing, feature engineering, and model
  training.
• Train and evaluate machine learning models; track performance using appropriate metrics
  and error analysis.
• Document methods, assumptions, and results clearly so others can reproduce your work.
• Collaborate with teammates to translate product questions into measurable data/ML tasks.

Qualifications Required 

• Currently enrolled full-time at Memorial University of Newfoundland in a graduate program such as Computer Science, Data Science, Computer Engineering, Software Engineering, Artificial Intelligence, Applied Statistics, or a closely related field.
• Proficiency in Python fundamentals and experience working with data tools such as
  pandas / NumPy.
• Understanding of machine learning concepts and experience with ML Libraries such as Scikit-learn and/or PyTorch.
• Comfort working with databases and writing SQL queries (joins, aggregations, filtering).
• Strong communication skills and willingness to collaborate and ask questions. 

Nice To Have 

• Familiarity with reproducible workflows (Git, virtual environments, notebooks vs.
  modules).
• Experience with cloud tools (e.g., AWS S3/EC2) or containerization (Docker).
• Exposure to data visualization or dashboards.

What You’ll Gain 

• Real-world experience shipping practical data science work in a startup environment.
• Ownership of meaningful pieces of an ML pipeline, from data to evaluation.
• A collaborative workspace where curiosity, initiative, and new ideas are welcomed.

Program Details

International students must comply with the allowable number of work hours as determined by
their immigration documents. (Study permit holders may work up to 24 hours/week off-campus,
provided all eligibility requirements outlined on the IRCC website are met.)

All applicants must follow this link to apply:

https://mun.jotform.com/260155415774256