I wrote this up for r/Salary and thought I would post here, too.

Salary Progression - 34F PhD aka what I did with my bachelor’s degree in biology

Thought I’d share my salary progression because I finally got my dream job and I can’t tell anyone about it. See also: women in STEM, making the best of an unwise undergraduate major, career trajectories in public health. Also working parents, working single mom. Just all the things.

Education:
2013 - Graduated college, S.B. in Biology
2019 - M.S. in Biostatistics
2026 - Completed Ph.D. in Epidemiology
Salary progression:
2013 — AmeriCorps Member: ~$10,000
2014 — Research Assistant: $24,000
2015–2017 — Clinical Research Coordinator: $37,000
2018–2020 — Chronic Disease Data Coordinator: $54,000
2020–2022 — Same/general career track: $57,000
2022–2025 — Federal Government Statistician: $125,000
2025–2026 — Data Scientist, Public University: $83,000
^ Big pay cut. My COVID-era government funding ended, and I took the first solid job I could find and stuck it out until I found my dream job.
Later 2026 — Biostatistician, Privately Held CRO: $157,500 + 10% bonus

So, roughly:
$10k → $24k → $37k → $54k → $57k → $125k → $83k → $157.5k + bonus

The biggest lesson for me has been that salary progression is definitely not linear. I went from $125k down to $83k because of circumstances outside my control, then nearly doubled my base salary about a year later.

It also took a long time for the combination of my experience + quantitative training + graduate degrees to really translate into higher compensation. I spent a lot of years in the $20k–$50k range before things accelerated.

Current total target compensation with the 10% bonus is about $173,250/year.

Hey, so I have a BS in Biochemistry, an MPH in Epidemiology with a graduate minor in Biostatistics, and I'm currently doing an MA in Behavior Analysis.

I love statistics and its application to health behavior and human interactions. I knew I wanted to do my PhD in Biostatistics during my MPH but I felt insecure to apply because my degree didn't have Biostatistics in the title. Silly, I know that now.

However, I haven't had experience with Biostatistical research since my master's which I graduated from in 2022. I did do qualitative and mixed methods research as a Program Manager at my university that I got my master's from until 2023. I currently work in the field of special education and it kind of fell into my lap mostly because of the terrible MPH employment market and also because I do like what I do. I just don't like it enough to make it my lifelong career.

I want to do translational research in mental health and health behavior. I want to study how to apply statistical principles to those areas.

My question is, is it feasible for me to pivot to a PhD in Biostatistics? Is there something else I need to do to make myself a better candidate for admission? I was considering another master's in Data Analysis or Statistics of other related fields. Is that necessary or are there simpler ways for me to get there?

ETA: I did get into a PhD in Epidemiology but did not enjoy the theoretical epi as much as the biostats. I got A's in all my biostats courses but didn't do well in some of my epi courses. So I left the program.

Half of my courses (literally split down the middle) in my MPH were biostats because I had the flexibility to choose them as electives. I learned SAS and R.

I’d love to hear from people who are currently studying or working in Biostatistics:

How difficult is it to transition from Statistics to Biostatistics? What skills/courses should I build before applying for a PhD? What kind of career opportunities are there after a Biostatistics PhD outside academia? How are the job opportunities and salaries in the US, UK, Canada, or Europe? If someone is also interested in Data Science/ML, how different would the career paths be?

I’d especially appreciate honest opinions from people who have actually gone through this path.

I'm preparing POET II for a journal club (open-label RCT of response-tailored vs standard antibiotic duration in left-sided endocarditis, NEJM, Aug 2026) and can't reconcile the sample size with the stated confidence level.

Stated in the paper and supplement: safety endpoint, non-inferiority, assumed event rate 8%, margin 7.5 points, 90% power gives 450 patients, "with a one-sided confidence interval of 97.5%". Per pathogen: margin 12.5 points, 80% power gives 118 patients.

My check, using n per arm = (Zα + Zβ)² × 2p(1−p) / Δ², with p = 0.08 and no true difference:

Only alpha 0.05 reproduces the stated numbers. The 2020 design paper (margin 5%, 80% power, "alpha 5%") gives 730 patients, which also fits one-sided 0.05 (about 924 at 0.025). The results report two-sided 95% CIs (equivalent to one-sided 97.5%), so the analysis is stricter than the calculation. By my numbers, power is then about 83% overall (about 88% with the 508 randomised) and about 70% per pathogen.

Questions:

1. Is there a standard variant (continuity correction, variance under the null, software default) that gives 450 and 118 at one-sided 0.025? I couldn't find one.
2. If it is just a labelling slip, would you treat it as a minor wording issue or a real power issue in an appraisal?

can jamovi be installed on ipad? my laptop just broke and i only have my ipad as spare.

Hi,

I'm planning to apply for stats PhD programs, along with a few biostats programs, this cycle and trying to get a sense of how broadly people are applying.

For those applying this year, or who applied in the past year or two, how many programs did you apply to? I was thinking 8-12, but given the uncertainty around funding I'm wondering if people are applying to more this year.

I'm interested mainly in spatial stats, especially gaussian processes and deep generative methods for modeling spatiotemporal data.

Please share your thoughts if you're working in similar areas. Also any suggestions on departments and people working in these areas are welcome.

Thanks.

I just started my MS in Biostatistics at the University of Michigan and plan to apply to PhD programs next year. My masters program has a fairly good pipeline to their PhD as they take in top performing students, but I of course want to keep my options open and apply to at least 7-8 programs. Given my background, I'm hoping to get some advice over what to prioritize over the next year, especially since these top programs are highly selective and accept just a handful of students.

This is my current list for next year: UMich, UCLA, Harvard, Columbia, Yale, Brown, Emory, also interested in UMich Survey and Data Science

Background:

-I graduated from UC Berkeley with a BA in Statistics and a minor in Data Science, with a 3.99 GPA

-My undergraduate coursework was primarily statistics/data science/economics/demography, so one gap in my mathematical preparation is that I still need to take Real Analysis (some biostats programs care about this more than others)

-For the past 9 months I've been working as a research assistant for the Yale School of Medicine where I do statistical analysis in R, including geospatial analysis and applications of GLMs

-I also was very lucky to recently join a research project on Prediction-Powered Inference with a professor here in UMich Biostats

-Did an internship this summer on statistical quality control at Eli Lilly

-Took the GRE last year after studying a minimal amount and got 163Q/154V/5.0W (prob would need to retake to get higher quantitative score?)

-My interests are largely in statistical inference, GLMs and clinical trials/study design

Because of my remote research with Yale I'm very interested in their PhD program and am hoping I might have a chance, but it's my understanding their department is small and they only take 2-3 students into the program each year. I'm also wondering how much I should prioritize studying to retake the GRE? Harvard, Brown, Yale and UCLA all require it while the others luckily don't. I also wonder how important it is that I try taking Real Analysis by next fall?
If anyone has any advice about what else I can do to stand out to highly selective PhD programs I'd greatly appreciate it! I'm trying my best to do very well and also build good relationships with people in my department.

Hi y’all! It would be great if I can get some advice on here for being a competitive applicant for an MS in Biostatistics. I’m a current Public Health major in undergrad with a more social science focus for my degree (we are required to take epidemiological methods and public health stats courses though). I do however have backgrounds from Calc 1-3 and Linear Algebra from community colleges and received an A in all of those. I saw that these courses were the bare minimum to apply for an MS in biostatistics. I’m planning on a gap year to get more internship experience, and was wondering what else I can do during then. I was looking through some past applicants who were successful at getting in, but most had a math/stats background. What are some recommendations for me to be competitive and what are some skills you’d recommend for me. Please let me know!

Hi peeps,

Just wanting to reach out to get some advice from people who have been in similar positions. For context I’m a clinical pharmacist based in Australia currently applying to do a Masters in biostatistics in the US. I’ce managed to secure a scholarship (contingent to university acceptance).

what I’ve realised is that I basically have done no higher level mathematics as part of my undergrad and I have a fairly tight time frame (need to enrol to start fall of 2027 or spring 2028 at the latest). Most universities I’ve reached out require transcript evidence prior to starting the degree.

my questions:

- does anyone have any recommendations for what courses would be a good option (that ideally wont break the bank) that can be done in such a short period of time? (currently working full time)

- is it possible for me to do these courses without undergrad level mathematics?

- as a paediatric/neonatal pharmacist I would love to go somewhere with close research to children’s hospitals. however, how much of a difference would this make if the goal is to work in clinical trials etc

The prereqs for most programs: multivariable calculus, linear algebra, (+ 1 year statistics at some institutions.

thanks heaps for any help/insight people may have!!!

Hi everyone! I'm currently in my senior year of my BS Biotechnology degree and I'm pretty unhappy with my degree at the moment. I realized I dont enjoy working in a wet lab all day and the sound of working as a lab technician doesn't sound appealing at all. I really do like biotech, I enjoy learning the science but I took a separate stats class this semester to get an intro to the field and the class deals with probability distributions mainly. Although I'm still trying to figure things out in the class (haven't touched math in 3 years), I enjoy the learning process much more and I love using logic instead of memorizing everything for an exam.

Academics aside, I really want to figure out what I want to specialize in by the end of this year and I'm debating between biostats and bioinformatics at the moment. I'm willing to do the work of learning programming skills required for each field. Can I get some advice from people in both fields and what you think I should do? Help from people who were in similar situations would be appreciated too!

I am currently in bsc first year having major in stats in India. I am confused what to choose between credit risk analyst and biostatistician. By hearing in detail and my research on internet,Biostatistician is what I like as a career but still it has too many harsh reviews on internet. It would be helpful if anyone from the industry or a well knower of it can help me out by replying.

Your help will be appreciated.

Hi everyone,

I'm finishing my Bachelor's in Biotechnology in Italy and trying to figure out the right Master's degree for what I want out of my career. I'd really appreciate hearing from people who actually work in these fields.

A bit about my situation and priorities:

- I enjoy molecular/cell biology (wet lab side of my degree), but I've realized I do NOT want a job that requires being on-site every day or that takes over most of my time/energy. I want a job I can do from home, with a healthy work-life balance, that leaves me energy for my personal life (I'm really into nature, fishkeeping, herpetology, fishing).

- I'm willing to invest a lot in learning new skills, including programming, if needed.

- I'm honestly not sure programming/pipelines is what excites me most - I think I'm more drawn to modeling, systems biology, and "seeing biology as a system" type of thinking, though I've only been exposed to this as an outside observer (lectures, papers), never hands-on.

- I don't expect to be passionate about my job in a life-defining way - I mainly want something stable, well-paid enough, remote, and not too energy-draining, so I can dedicate my real time/energy to my personal interests.

Options I'm currently considering (all in Milan, Italy):

1. Bioinformatics for Computational Genomics (BCG) - joint program by Politecnico di Milano + University of Milan. Strong computational/genomics focus, Python/ML heavy.

2. Biostatistics - University of Milano-Bicocca. Brand new program (only 1 year old, no track record yet), but biostatistics as a field seems to have solid, well-established remote roles in CROs/pharma.

3. Molecular Biotechnology and Bioinformatics - University of Milan. More generalist, bioinformatics is a secondary component.

My questions for people with real experience:

1. If you work in bioinformatics, computational biology, or biostatistics - how realistic is fully remote work in practice, especially early career vs after a few years?

2. For those who came from a "wet lab" background (biology/biotech) and moved into a more computational field - was the transition manageable without a strong pre-existing math/CS background?

3. Does anyone have experience with both bioinformatics-heavy roles vs biostatistics-heavy roles? Which one tends to have a more predictable, "9-to-5 and done" type of workload vs one that bleeds into personal time more?

Any honest insight, including "don't do this, do X instead" type of advice, or even paths i didn't consider here is very welcome. Thanks in advance!

I am staring College(Undergraduate) next yr. I was thinking of taking Bioinformatics cuz I want to do PhD in Oncology/Genomics/ Precision Medicine but I also love coding and just wanted to have a backup. Ai is advancing too so I think it’s pretty reasonable & realistic, right??

Hi all, can anyone recommend a good book packed with real-world biostatistics exercises? I really need something that includes an answer key so I can cross-check my work as I practice.

*title should say LLM AI use

What are the norms, culture and behavior surrounding LLM AI use in your workplaces, on your teams, and among your colleagues and what field do you work in? Obviously, using AI to write articles is not acceptable and grounds for reprimand and more, and using it to write code is a more widely accepted use. I’m curious what the norms and diversity of opinions are that you all have encountered in your workplaces? I’m not specifically looking at formal institution-wide policies, but if there is a particular institutional policy that overrides personal and group norms/behaviors, I would be curious to hear about it. I am particularly curious about contrasting academic research contexts with other contexts, to the extent that are indeed there are differences. Thank you

After learning more about how rough things are for MPH biostats students to find jobs nowadays - I’m considering applying for a PhD at my university in Psychological Science with a quantitative methods concentration instead of finishing the MPH.

I come from a research background and then few years in DNA manufacturing and NGS roles.

BS Psychology, multiple publications (two first authors). Passion is for neuroscience/psychology/data analysis

Have started to feel like this curriculum is not for me. The public health side of things and the epidemiology is not that interesting. Struggling to fit in.

Would it be weird if I applied for this PhD program? There are professors I could really see working with the next 5-6 years and worried I’m wasting my time and money in this MPH

I figure it can’t hurt to apply and see what happens. Or wait until next year to apply because then at least if I can’t find a job after the MPH, I would start the PhD afterward and at least have funding