Data Scientist - Learnvia

Learnvia is a new Carnegie Mellon University initiative focused on transforming post-secondary math education through research-driven, AI-enhanced courseware. As part of CMU’s broader efforts in learning science and educational technology, Learnvia collaborates closely with faculty, researchers, and institutional partners to develop innovative, evidence-based learning experiences.
We are seeking a Data Scientist who is passionate about improving student success outcomes using insights from data. In this role, you’ll work closely with product managers, content developers, engineers, and researchers to analyze learner behavior, evaluate instructional effectiveness, and develop models that improve student success at scale. This role sits at the intersection of data, learning science, engineering, and product—ideal for someone who wants to shape how modern learning platforms measure and accelerate knowledge and skill development.

Core responsibilities include:

• Analyze learner behavior and engagement using clickstream, assessment, and other data to identify patterns that drive success outcomes.
• Build predictive and causal models to forecast performance, detect at-risk learners, and evaluate the impact of learning interventions.
• Design and analyze A/B tests and other experiments to measure the effectiveness of new features and instructional strategies.
• Develop data products, such as personalized recommendations, mastery models, or early-warning indicators.
• Apply learning science models (e.g., learning curves, item response theory, knowledge tracing) to track skill growth and predict mastery.
• Identify where learners struggle and what drives retention, progression, and engagement.
• Collaborate with cross-functional teams (engineering, product, learning design, customer success) to translate data insights into product improvements.
• Create dashboards and reporting tools that support both internal teams and external partners (instructors, institutions, or enterprise clients).
• Communicate findings clearly through visualizations, presentations, and written summaries tailored to non-technical audiences.
• Support data quality and governance by helping to shape data pipelines, instrumentation, and documentation.
   

Adaptability, excellence, and passion are vital qualities within Carnegie Mellon University. We are in search of a team member who can effectively interact with a varied population of internal and external partners at a high level of integrity. We are looking for someone who shares our values and who will support the mission of the university through their work.

You should demonstrate:

• Knowledge of learning science research, best practices in pedagogy and andragogy, and research-based principles of multimedia design is a plus.
• Strong storytelling and communication skills and an ability to turn complex analyses into actionable insights for non-technical audiences.
• Ability to work independently and collaboratively.
• Curiosity about how people learn and how data can improve learning experiences.

Qualifications:

• Bachelor’s or master’s in Data Science, Statistics, Computer Science, Cognitive Science, Learning Sciences, or a related field and at least five (5) years of experience. PhD a plus.
• Experience analyzing learner or user interaction data in large-scale learning platforms (higher ed learning platforms is a plus).
• Strong analytical and statistical skills (SQL, Python, R).
• Experience with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Familiarity with causal inference methods (e.g., matching, RCTs, diff-in-diff).
• Experience building dashboards (Tableau, Mode, etc.).
• Experience with cognitive modeling, learning curves, or educational data mining.
• Prior work with large data sets; experience in edtech, LMS analytics, or behavioral/log data preferred.
• Experience designing or analyzing experiments (A/B tests).
• Background in recommendation systems, mastery learning models, or student-success analytics is a plus.
• Familiarity with mathematics instruction at the higher education level is a plus.
• A combination of education and relevant experience from which comparable knowledge is demonstrated may be considered.

Requirements:

• Successful pre-employment background check

Additional Information:

• Sponsorship: Applicants for this position must be currently legally authorized to work for CMU in the United States. CMU will not sponsor or take over the sponsorship of an employment visa for this opportunity.  Carnegie Mellon is not a qualifying employer for the STEM OPT benefit: only the 12-month OPT may be used to work at Carnegie Mellon.
• This a fixed-term position with Carnegie Mellon

Joining the CMU team opens the door to an array of exceptional benefits.

Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions. Unlock your potential with tuition benefits, take well-deserved breaks with ample paid time off and observed holidays, and rest easy with life and accidental death and disability insurance. 

Additional perks include a free Pittsburgh Regional Transit bus pass, access to our Family Concierge Team to help navigate childcare needs, fitness center access, and much more!

For a comprehensive overview of the benefits available, explore our Benefits page.

At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained through education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it’s about finding the perfect fit for your professional growth and personal aspirations.

Are you interested in an exciting opportunity with an exceptional organization?! Apply today!

Location

Pittsburgh, PA

Job Function

Data Analysis

Position Type

Staff – Fixed Term (Fixed Term)

Full Time/Part time

Full time

Pay Basis

Salary

More Information: