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Harvard University

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AA&D: Good pay and work/life balance; little room for advancement - Development Assistant Harvard University Employee Review

3.0
30 Dec 2021
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Pros

Good salary compared to other university and nonprofit jobs. Office culture is professional and organized. Colleagues are generally friendly and social, and knowledgeable about their work. Wide offering of professional development trainings, and abundance of resources to do your work. Benefits are excellent and there's a culture of only working within set hours, providing work/life balance.

Cons

Hierarchical with little chance for advancement, people either move up or move out and management will not make the effort to retain you. Some gatekeeping of roles and often people are overqualified for the job they're hired for, due to the "prestige" of working at Harvard. Salary does not take previous experience into consideration enough. This office is not diverse compared with the student and local population. Frontline fundraising teams are prioritized in many ways over support/resource teams. Jobs are highly specialized, meaning you will be doing the same few tasks over and over.

Explore other reviews about Harvard University

5.0
25 Jun 2026
Recommend
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Pros

A PhD is always very advisor/PI dependent, but I had a great experience with room to learn, grow, and explore interesting research questions. As with any PhD program you’ll work pretty hard for relatively lower pay and perhaps less directly applicable industry career trajectory at the end, but if you find good people to tackle the journey with and get to work on interesting problems I personally think the journey is worthwhile!

Cons

Less of a program wide cohort in my particular engineering field. Some funding challenges with the govt last year but seem mostly back now, apart from the recent administration issues funding wasn’t generally a challenge

5.0
3 Jun 2026
Recommend
CEO approval
Business outlook

Pros

You work with experts from every field possible in science from Mechanobiology to Metallurgy and working as a ML intern along with scientists from different fields gave me a lot of exposure and how to work in a research department.

Cons

One challenge encountered was maintaining a clear focus on training objectives, as the evolving parameters required continuous reassessment of the model's learning priorities. Additionally, the absence of established ground truth for the domain presented a limitation in validating and guiding the approach effectively.

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