Operations Research Data Scientist

Paper | Montreal or Remote (Canada) | Senior | Full-time

Paper is looking for an Operations Research Data Scientist to join our growing R&D and analytics team. The hire will be responsible for researching, developing, and implementing state-of-the-art O.R. applications services around scheduling and managing our fast-growing tutor base. The data scientist will be responsible for identifying operations enhancement opportunities and ensuring the success and relevance of O.R. projects. They will spend a significant amount of time planning, documenting and evaluating the results of their experiments, working closely with our research scientist. They must be self-directed and comfortable conducting applied research in collaboration with a wide range of stakeholders and functional teams. The ideal candidate will have a PhD level background in operations research, mathematics, or related field.


  • Work with service managers, workforce planners, and other stakeholders to identify study requirements and develop relevant OR technical approaches

  • Develop new OR products and solutions to support optimization of demand prediction, supply management, tutor scheduling, and quality assessment

  • Manage and contribute to the development and execution of OR study plans

  • Ensure application of OR best practices for all service analysis efforts

  • Works with other data scientists and developers to integrate the methods and solvers into decision support systems for the service department

  • Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive operational improvements.

  • Assess the effectiveness and accuracy of new data sources and data collection techniques.

  • Develop custom data models and algorithms.

  • Develop company A/B testing framework and test model quality.

  • Coordinate with different functional teams to implement models and monitor outcomes.

  • Develop processes and tools to monitor and analyze model performance and data accuracy.


  • We’re looking for someone with experience manipulating data sets and building statistical models, ideally with a PHD in Operations Research, Mathematics, Statistics, or related quantitative field.

  • Strong problem solving skills with an emphasis on product development.

  • Track record of developing and employing parametric or nonparametric probabilistic models of static/dynamic data or systems.

  • Strong knowledge of a variety of analytical and computational methods, including classical and modern statistics, mathematics, graph theory, differential equations, and linear algebra.

  • Strong programming skills in Python, Java, C++, C#, R, MATLAB or Simulink.

  • Experience with applying mathematical principles to develop deterministic, stochastic, or rule-based models of systems or networks.

  • Experience with applying data-analytic techniques to analyze, visualize, and summarize complex data to draw conclusions.

  • Experience with modelling demand and supply prediction for part-time workforce planning is a big plus.

  • Excellent written and verbal communication skills for coordinating across teams.

  • A drive to learn and master new technologies and techniques.

About Paper

Driven by the mission to democratize education, Paper is the leader in personalized learning. Partnering with innovative schools and school districts, Paper helps deliver true educational equity through their category-leading Educational Support System (ESS) that offers virtual access to 24/7 tutors and essay reviewers. Founded in 2014, Paper philosophically believes that all students should be given the tools and resources to reach their academic potential, independent of socioeconomic status, geography, language, or other barriers. Today, Paper is partnered with over 700 schools and supports over 750,000 students. We are headquartered in Montreal, Quebec with remote employees across the US and Canada. Paper is proud to have been named by GSV as one of the most transformational growth companies in digital learning.