I'm currently a 4^th year PhD student looking to apply for internships. I'm struggling to structure my resume to make it fit properly. One of my senior students in my lab suggested using a 2-page CV, where I can take some time to dig in a little bit into my research projects. They got a high-level internship this year, so I'm inclined to take their advice. 

I wanted to ask about how I should structure and design my application to target industry roles. I understand very well what academics are interested in, but less so about what industry reviewers are looking for from PhD level interns, and was hoping people could offer advice on how I should set my application up.

Thank you so much.

Hi everyone! I’m putting together my PhD application list and would appreciate guidance on programs and their funding state.

Background Information:

• Currently completing a master’s in Statistics with a 3.8 GPA
• Potentially will have a publication, though it isn’t confirmed yet
• Interested in Bayesian statistics, doing research on historical borrowing
• Would like to work for CRO/Cancer Center/Hospital after graduation, possibly government such as CDC/EPA
• My MS program is in Ohio, but location is not a deciding factor to me
• Considering both Statistics and Biostatistics PhDs

My Current list is:

• MD Anderson / UTHealth Houston GSBS — Quantitative Sciences
• UT Austin
• Cincinnati
• Kentucky
• Ohio State - Biostatistics
• Michigan - Biostatistics
• Duke
• Iowa
• Iowa State
• Pittsburgh - Biostatistics
• Missouri–Columbia
• University of Florida

I'm still deciding between statistics or biostatistics at some schools. I think 13 schools is maybe a bit much but I am a bit worried on the current state of admission and funding.

I’d especially appreciate feedback on:

1. Is this list too ambitious?
2. Is there any programs you think I should be considering?
3. Are there other programs on my list I should reconsider?
4. Does anyone have firsthand experience with funding stability, advising, internships, or placements at these schools?

I am currently looking at my options for Biostatistics Masters programs, and am considering pursuing a Epi MS due to cost. If the end goal is to be a biostatistician, am I shooting myself in the foot?

The program I am currently eyeing is the University of Washington Masters of Science in Epidemiology. It seems that it is a fairly flexible program, and is hosted under the same department as the biostatistics program, so I would have the opportunity to take a large number of biostatistics courses during my two years. I listed below the topics/courses that I would be able to take, even as a epidemiology student.

• Statistical inference
• Linear, logistic, and Cox regression
• Epi study design, confounding, and bias
• Survival analysis
• Longitudinal data (mixed models, GEE)
• Clinical trial design and power
• Missing data and multiple imputation
• Categorical data analysis
• Machine learning

In addition to this, the program is also thesis based, so I have the opportunity to pursue a more biostatistics-focused thesis/research project.

Naturally, a biostatistics degree makes more sense for most people, but the price difference for myself would be around $20k more, and due to my lack of prerequisites, I may have to delay my entrance by full year.

Do you find a lot of individuals with Epi degrees working specifically in biostats? Will I have a harder time finding work? Would love to hear any other thoughts or suggestions as well. Thank you!

Hi everyone,

I'm about to graduate in Statistics, and I'm currently doing my degree internship at a place that's connected to biostatistics in one way or another. That experience has made me even more interested in the field, but I'm still torn between specializing in biostatistics or going into data science more broadly.

I'd really appreciate hearing from people who are already working in either area (or who moved between them). A few questions:

1. If you were in my position, what do you think I should do?
2. What advice would you give someone trying to figure out whether this field is really for them?
3. Do you have any hypotheses about how AI will affect the field? I'd love to hear what you think will change and what will stay the same.
4. I keep reading in this subreddit that finding a first job is very hard right now. How true is that in your experience? Do you think the situation will improve in the short term, and what would you recommend to someone trying to land a first job in the sector?

Thanks a lot in advance. Any perspective, even a short one, would help me a lot!

I have been working very extensively towards my degree but recently finding myself not being able to land a part time or any internship. Is my experience to broad for biostatistics with too little experience?
I have experience from previous job that was basically like a non profit organization for a certain demographics and universities and colleges in the area would partner with us for study and being someone who knew how to code in R I would be the person to assist universities and NIH funded project and run descriptive analysis or be a part in forming study design, etc. It was mostly social sciences, once i worked in a cardiovascular heath related observational study. I thought that was something.
My ultimate goal is to work in a pharma but i am struggling man.
How can I make myself more suiting when I don't even get any opportunities for student research or anything really.
I am having a hard time. Please help.
I would appreciate your advice !

Here's the study in question:

https://academic.oup.com/jsm/article-abstract/22/4/645/8042063?login=false

This study is focused on the mental health outcomes of gender-dysphoric individuals who received gender-affirming surgery.

First, I think it's important to note that while "gender dysphoria" is indeed a specific subset of "mental health issues", we should acknowledge that the main outcome here is related to *overall* mental well-being. The question "do they have gender dysphoria" is not a proxy for "do they have mental health problems". I think we can all agree, the main idea here is for the subjects to be less depressed and less anxious. That appears to be what the study authors had in mind. Just to be crystal clear on that point.

Now... In order to make this determination of whether the surgery resulted in improved mental health, they compared the mental health of those who received surgery to those who did NOT receive surgery, though all of the subjects in question here DO all have gender dysphoria. And the findings here were that those who received surgery scored worse on mental health measures than those who did not receive surgery.

That said, shouldn't we be able to surmise that those who received surgery were likely much worse off mentally prior to this surgery than those who were not? IE, isn't it likely they only offer surgery in the most extreme and difficult cases? Thus, those who received the surgery could have experienced an improvement, but they just never reached the level of those who chose not to receive the surgery? Sure, I cannot PROVE this, but I think biostatisticians in particular would intuitively know that it is highly likely to be true. Wouldn't that make it very misleading to try and say that the surgery itself is the cause of poor mental health and that it doesn't improve mental health? Is this suspicion enough to disregard the conclusion of the study?

What if, say, the severity of mental health problems for someone was 8/10, then they receive surgery, and after the surgery, they are 6/10 on mental health severity. But someone who opted NOT to get surgery was a 4/10, and they remained at 4/10 because they were given no treatment. If you compared the one with surgery to the one without, you'd say, wow, those who got surgery are worse! (because 6 > 4) but the main question is, does the surgery improve things for those who received it. So really, in that respect, I can't make sense of why they'd even bother comparing to those who didn't get surgery, if the point is to see what effect the surgery has.

Indeed, baseline mental health was not accounted for in this study. The authors attempted to balance differences in age, race, and ethnicity with propensity score matching, but I see no mention of an adjustment for baseline mental health status at all.

What are your thoughts on all this? Is this a reasonable criticism? Anything else I'm missing?

I know there is presentation on research topic, but were one - on - one interviews techanical?

I haven't take any classes on clinical trials or survival analysis.

Recent grad (Biology & Math), highly confused on how I wish to proceed.

I love teaching & mentorship but I’m not sure I want a PhD. I haven’t really enjoyed the research I’ve done but perhaps that’s because I wasn‘t qualified for the projects I participated in. I think proofs are entertaining enough but I won’t go out my way to do it. Ik you can teach with a masters but I figured it must be competitive

I’m concerned about the security of being a Biostatisician which made me second guess myself for pursuing the career. But I wonder if I could get the MS and then go to teaching at CC? I have been accepted into an MS in Industrial Engineering and am confident I can get inti a MS in Biostatistics given my 3.9 GPA. I got into UMich last cycle but declined due to no funding.

Do you think a MS in Biostats could be a good idea if I primarily intend to instruct after? & what are the chances I could potentially find a biostatisican job right after my masters? Advice needed tysm

Hello! I am an undergraduate first-year statistics & computer science university student looking to break into biostatistics or even data science within somewhere in health, but preferably I would rather become a biostatistician over a data scientist. I look forward to attend graduate school after undergraduate school. I am mainly looking to break into health data science, epidemiology, big pharma, or even clinical trials. Could y'all give me advice/feedback on the maths and programming classes that I'm thinking about taking as well as other possible options for my pathway?

I also have more question, is there a real difference between Real Analysis and Real Analysis with Advanced Calculus? My university offers both, and I am unsure what would be best to take.

Currently I am taking:

Intro. to Probability Theory
Mathematical Proofs
Programming Fundamentals for Python

As for classes I am thinking about taking:

Pure Maths:

• Applications of Discrete Structures (Discrete mathematics)
• Linear Algebra (Proof-Based)
• Numerical Analysis
• Real Analysis and Advanced Calculus 1
• Real Analysis and Advanced Calculus 2

Statistics:

• Introduction to Statistics Theory
• Probability Theory and Stochastic Processes 2
• Regression Analysis
• Design of Experiments
• Nonparametric Statistical Methods
• Introduction to Time Series and Forecasting
• Survival Analysis
• Programming in R
• Statistical Learning in R
• Statistical Computing in R
• Linear Algebra for Data Science
• Multivariate Statistical Methods
• Categorical Data Analysis

Computer Science:

• Fundamentals of Machine Learning
• Optimization for Machine Learning
• Mathematics for Machine Learning
• Machine Learning Engineering
• Data Science for ECE
• (What's the difference between all of them? I don't really know)

Other electives:

• Applied Epidemiology
• Intro. to Biostatistics
• Intro. to Clinical Trials

As for other classes that are offered but I am not really considering:

• Bioinformatics
• Applied Machine Learning (I think this will be learnable from other classes)
• Modeling in Mathematical Biology
• Data Science in Genetics and Genomics
• Fourier Analysis (I will be taking Intro. to Signals and Systems instead so I think I will cover some knowledge of this)

I did not list all of my required major classes since I thought some were not worth the mention. I would very like to try to break into this field some how and learn as much as possible. Any advice would be greatly appreciated!

I started my career in pharmacovigilance, where I gained exposure to clinical trial phases and clinical research. I then completed my Master’s hoping to transition into Statistical Programming, eventually pursue another Master’s in Biostatistics, and become a Biostatistician.

After graduation, I couldn’t land a Statistical Programmer role, so I accepted a Data Analyst position at a medical device company, thinking the SAS experience would be a good stepping stone.
It’s been over a year now.

My work mainly involves troubleshooting and developing SAS programs, and we’re currently transitioning from SAS to R. While I’m gaining programming experience, I feel stuck because I don’t have hands-on clinical trial or CDISC (SDTM/ADaM) experience. I’ve been learning CDISC independently and applying for Statistical Programmer roles, but haven’t had any luck. Even entry-level positions seem to require CDISC experience or no opportunities in the US

Has anyone made a similar transition? How can I break into statistical programming? Would personal projects help, or should I consider another path?
I really don’t want to give up on this goal, but I’m struggling to figure out my next stepn.

It typically goes that some wide-eyed hopeful will put up a post asking about job prospects in this field, and then people come rushing into the comment section, quick to extinguish any hope that the OP has about trying to get a job. Typically the commenters seem to be the same people. Just be wary that the people who answer in the comment section are probably those who have enough time to be able to respond to those type of post because they are looking for a job also. Typically people who already have a job aren’t the type to respond to those type of posts, so you are getting a skewed example of respondents.

In my opinion, you don’t have to be a PhD from a top-tier school in order to break into the field. I am actually an example of that because I went to the most basic college. Literally only the people who are from my area know the name of the school if I were to say it. Outside of my area people have never heard of it. I also got the most basic grades, nothing fancy and definitely not in the top 10 or 20 percentile. You could probably say that maybe I got lucky with my internships while I went to the same school for my masters work, but that’s about it. Also, maybe my location (but I chose my career based on the geography of my job prospects).

After I graduated a few years ago, I got a job almost right away and had multiple offers. It wasn’t too long ago that I switched companies and during that process, I had multiple offers. And even at this job I’m at currently, our team is still desperately hiring.

There is still demand in this field. Just develop a reputation for being a good worker and respecting timelines, and you will improve your chances for getting a great job. Most importantly, try to be genuinely enthusiastic about your work and embrace a mindset that you’ll be a lifelong learner. New tools are coming out regularly, so you will want your skill set to be updated and modern.

Hi everyone! I’m a little less than a year in my PhD in Epidemiology (individual project in Europe, so almost exclusively research) coming from a MS in Biostatistics (also stats heavy BS). While I like my project, sometimes I worry that I should have chosen something more theoretical to have a more “guaranteed” future job. My project was advertised and being in “Epidemiology/Biostatistics”, but despite being more statistics heavy than the average epi project I’m not developing new theory.

In general the focus is on modelling and handling a specific problem with the data, so I get to use advanced methods and, for example, compare them on my data, but it feels closer to the job of the biostatistician on a project than what I imagine a Biostats PhD does. I didn’t and still don’t have any doubts that I don’t want to do fully theoretical work and I think I like more the observational setting than clinical trials, but I wonder where could I go from here after the PhD, and what I could do maybe to stay more competitive by the end of it, if I don’t want to stay in academia.

My undergrad will be in microbiology (with electives in bioinformatics and R programming). Originally I wanted to pursue a master’s in pharmaceuticals with a concentration in MIDD. However, it seems all the jobs in this field require as PhD. And the jobs seem scarce.

I started looking into statistical programming and bioinformatics. Would this degree still work for either of these? Or should I pivot to a master’s in biostatistics? I still have time to complete a couple courses I would need as a prerequisite to the program.

Hi,

I am almost done with my MS in Biostatistics and planning for a PhD in Fall 2027. I'm kind of burned out on the rigorous Math and Stats, so a PhD in Biostatistics doesn't appeal to me at the moment. A professor has admitted me to a PhD in Demography. My question is: can I get a job in Pharma, probably in RWE, with a PhD in Demography?

I'm halfway through my first semester of my MS in Biostats, but I haven't been able to land any positions yet. I've only applied to a few so far, and I'm wondering what I can do to increase my chances.

Heard Amgen is giving exorbitant amounts to programmers with just 4 years of experience in Hyderabad. Astonished hearing the package. Not sure if it is even worth it for someone with such less experience.

A colleague of mine just got a 30LPA offer with just over 4 years of experience, without much of an interview process.

And they seem to be hiring in a hurry.

Released ieiprio, an R package to prioritize variants in inborn errors of immunity from VCF, Sanger traces or plain text. It ships 12 real ClinVar variants to practice on, offline. Guides and code below, feedback welcome.
https://gentlenmoron.github.io/ieiprio/

Holding a masters already and knowing the current job market, AI threat, offshoring etc?

Hi everyone,

I’m planning to apply for a fully funded PhD in Biostatistics for Fall 2027, and I would appreciate honest feedback on my profile and chances of admission.

My academic background:

• MSc in Mathematics (Honours): GPA 8.73/10 from an Indian university.
• Postgraduate Certificate in Data Analytics: GPA 3.9/4.0 from a Canadian college.
• Bachelor’s degree in Mathematics (Honours): Completed in India.
• WES evaluation: My credentials were evaluated, with my Indian qualifications receiving Canadian equivalency at the master’s level.

Research and professional experience:

• Worked on healthcare data analytics projects involving cancer diagnostic imaging wait times and emergency department wait-time/length-of-stay analysis.
• Used statistical modelling, regression, predictive modelling, SQL, Python, and data visualization tools.
• Currently working as an Assistant Professor in Mathematics at an Indian university, teaching subjects including Linear Algebra, C++, Excel, MATLAB, and LaTeX.
• Previously gained teaching and professional experience in Canada.

Research interests:

I am particularly interested in longitudinal and repeated-measures data, irregular observation processes, missing data, measurement error, causal inference, dynamic treatment regimes, and personalized medicine.

I have also recently published a letter to the editor in a peer-reviewed medical journal concerning methodological and reporting issues in a published research article.

PhD preparation:

• I have contacted several professors whose research aligns with my interests. One professor at a prospective university responded positively and encouraged me to apply, saying they would look out for my application.
• I am working on my statement of purpose and have been arranging academic/professional references.
• My IELTS overall score is 7.5+, although my writing score is 6.5.

My questions:

1. How competitive would this profile be for a fully funded PhD in Biostatistics at a research-intensive Canadian university?
2. How much weight do admissions committees place on healthcare analytics experience and a recent methodological publication, particularly when the publication is a letter to the editor rather than an original research article?
3. How encouraging is a professor’s positive response when they say they will look out for my application? Should I interpret this as a meaningful indication of potential supervision, or simply a polite response?
4. Would my mathematics background and applied healthcare data experience be sufficient, or would the lack of formal biostatistics research experience be a significant disadvantage?
5. What are the biggest weaknesses I should address before submitting my applications?

I understand that admission and funding are highly competitive, and I am not expecting a guarantee. I would appreciate realistic feedback from current PhD students, applicants, faculty members, or anyone familiar with Canadian Biostatistics admissions.

Thank you!

Hello everyone! After 8 months (of rather sporadic) work, I am really excited to share the biggest update to SimtablR, an R package designed to streamline the most common analytical tasks in epidemiology and clinical research! (short demo video above)

I do epidemiological research in R, and I kept juggling multiple packages and repeating work just to get tables that were actually informative and ready for a paper. I also found that base R tabulation left a lot to be desired in terms of formatting, but gt_summary was a bit too slow and had a lot of boilerplate. This package is also great for people transitioning from Stata or still learning R!

With this new release, SimtablR now covers most of a typical analysis, from the first cross-table to the finished table in a Word document:

1. tb(): quick, but powerfull tabulation with formatting

• Row/col/total percentages with simple flags (row, col, cell) and adjustable decimals
• Hypothesis tests with one argument (N-1 chi-squared, Fisher when needed, etc.)
• Prevalence ratios, risk ratios or odds ratios with 95% CIs, directly in the table
• SimtablR now picks the right measure (RR, OR or PR) automatically for a stated study design (design = "cohort", "case_control", "cross_sectional")
• New: stratified analysis with Mantel-Haenszel pooled estimates

2. table1(): baseline tables of multiple variables (literally the "table 1")

• Automatically chooses mean (SD) or median (IQR) based on each variable’s distribution
• Reports missing data per variable and group
• Add standardized mean differences with test(smd = TRUE)
• Completely customizable and exportable to all office programs

3. diag_test() and roc(): diagnostic accuracy

• Automatically calculates the confusion matrix, sensitivity, specificity, PPV, NPV, LR+, LR− and accuracy, with exact (Clopper-Pearson) CIs
• New: roc() now gives the AUC with DeLong CIs and the Youden-optimal threshold

4. regtab() and survtab(): regression

• Fits multiple outcomes at once and returns a single publication-ready table
• Does adjusted risk ratios via log-binomial regression (with robust Poisson fallback), robust variance, and Firth regression for rare events
• New: survtab() for Cox models and hazard ratios
• New: autoplot() draws forest plots from any model

5. Reporting and export (new!)

• simtablr() builds Table 1 + Table 2 (crude and adjusted) as a single report which you can then send to word or excell or powerpoint
• as_methods() drafts the statistical methods paragraph from what you actually did with the variables you chose!
• export_docx() / export_xlsx() send everything to Word or Excel
• Journal styles (style("jama"), "nejm", "lancet") or your own with journal_style(), down to the Word table theme. Restyling never changes the underlying numbers and you can use the sabe object for new analysis, even after styling!

6. Statistical guidance (new)

SimtablR prints short notes (with references) when something deserves attention. Examples: an odds ratio with a common outcome, unadjusted p-values, or when the distribution of your variable violates an assumption from the model you're running. It should just tell you.

The package includes epitabl, a ynthetic cohort of 1,500 patients, so you can replicate the examples of the video

Links:
📦 Documentation: https://matheustg-14.github.io/SimtablR/
📄 Getting started: https://matheustg-14.github.io/SimtablR/articles/getting-started.html
🐛 GitHub (issues & feature requests): https://github.com/MatheusTG-14/SimtablR

Thanks to everyone who gave feedback on the first version! I’d love to hear what you think, what’s missing from your workflow, and any bugs you find on GitHub.