One researcher. Every paper has an author you can write to.
We are deliberately small. Every published result has a named person who did the work and who will answer a question about the method, rather than a press address that routes to nobody.
Who works on what.
Mohit Bhardwaj
Head of Analytics · Predictive modelling · Data quality · Causal inference & efficacy · Fairness & measurement · Curriculum & capacity · Language & student voiceLeads the analytics group and the research programme: the institution-specific models, the validation gates that stop a weak model reaching an advisor, the termly efficacy studies, the subgroup fairness reporting that gates every deployment, and the topic analysis over student feedback. Sole author of the six reports listed below.
- Early-warning model calibration across 14 institutional contexts · EPR-2026-04
- What actually worked: efficacy of 220 student success initiatives · EPR-2026-03
- Equity gaps in early-warning systems: who gets flagged, and who gets missed · EPR-2026-02
- Course bottlenecks and on-time completion: where students actually get stuck · EPR-2026-01
- Data quality is the binding constraint, not model choice · EPR-2025-02
- What students say against what they do: topic analysis on 340,000 feedback responses · EPR-2025-01
People who tell us when we’re wrong.
Three external advisors review methodology before publication. They are paid for their time, they hold no equity, and they are free to disagree with us publicly. Two have.
Prof. Meera Iyer
Educational measurement, Indian Institute of Education (visiting)
Reviews study design before publication and has twice told us a result would not survive peer review.
Dr. James Okonkwo
Learning analytics, independent
Advises on the fairness auditing programme and the limits of what our gap definition can establish.
Anjali Varma
Data protection and student rights, independent counsel
Advises on what our processor commitments should mean in practice, including where they cost us revenue.
Research roles here ship.
Every area lead also owns the corresponding production system. The measurement and the implementation are the same job, which is either the appeal or the objection depending on what you want from a research post.
You publish what you find
No internal review can suppress a result for being commercially inconvenient. Two of our six papers contain findings that are bad for us.
Prior work stays yours
We do not assign anything you built before you arrived.
Real institutional data, real constraints
You will work with live student records under processor obligations, which is more interesting and more restrictive than a public dataset.