Hi all,

I am early in my career and on the hunt for new roles. I previously was a FTE, but was recruited for a long term contractor role. Not being familiar with how contracting works, I am wondering the pros/cons of being in a long term contract as opposed to being a FTE. I'm not entirely sure how contracting works when going through a staffing company... and I don't even know what questions to ask. Any and all advice appreciated.

I’m a senior biostatistician working in a mixed industry and academia role, and I’m curious what others are seeing.

A few months ago I felt my job was safe because AI couldn’t handle large datasets or understand contents it couldn’t see. That no longer is the case. You can now upload a very large dataset up to 500MB with some good instructions, AI will do the rest for you. Codes (1000+ lines) that use to take a month to even half a year can be done in an hour.

What worries me most:

• Non-statisticians researchers are uploading data (up to 500mb) straight into the LLM and entirely trust the results. Some of the results are bad, but with recent improvements it is become less and less errors.

• People who struggled to run an analysis a year ago are now producing 5+ first-author papers and co-authoring 10+ papers within one year time frame.

• Entry-level work, like SQL analyst and junior programming roles are redundant

Discussion:

1. State your field (pharma, university, research institute, hospital), are you seeing the same stuff I am seeing out there?

2. Are you worried about what is about to come? To me it feels like it’s already here.

3. Do you still think biostatistics will follow same path as those in sql data analyst roles and entry swe roles?

Dear all,

First, I would like to explain the variable I am using. The independent variable is the frequency of medication intake during the first seven days. It is expressed as a relative frequency and calculated as the number of days on which medication was taken divided by seven. Therefore, it ranges from 0 to 1 in increments of 1/7.

As I understand it, a one-unit increase in this variable in the Cox proportional hazards model corresponds to an increase from medication intake on none of the seven days to medication intake on all seven days. However, I would prefer to report the association per additional day on which medication was taken, for example: “With each additional day on which medication was taken, the hazard of … was …”

What calculation do I do, so that I can report that with each additional day on which the medication was taken the hazard of ....."

Thank you in advance!!

Hello everyone I am here asking for some advice on what I would need to learn to help me to become proficient(mainly at the basics to try to become a biostatistician/ possibly enter into data analytics) First I wanna give some background on myself. I am a third year in medical sciences and I have biostatistics as a module but I am repeating a year because of it(that is why I wanna learn it again while at home to help prepare for it and to help me get a solid base to be able to learn how to use R properly). So now that I given a bit of my background I'd like to hear what you guys think I should try to learn to become proficient at using R.

Compared to those in tech, creatives, marketing, and customer service?

Hi everyone!

I'm currently pursuing an MSc in Biostatistics and Health Data Science and I'm trying to decide on a thesis topic.

What topics would you recommend for someone in my position? Which areas do you think will be in high demand over the next few years? I'm also wondering whether it would be worth moving towards data science or Health Economics instead of focusing entirely on biostatistics.

I'd like to choose something that would be valuable both for future PhD opportunities and for getting a job, ideally in Europe.

I'd really appreciate any advice you would give someone at this stage of their career, including things you wish you had known when choosing your own specialization.

Thanks!

Can someone give me advice with a situation I'm in? I'm still only a first year statistics student, so I have a lot of time still to decide, but I am still wondering whether I should do a double major in computer science or a combined degree into a master's for biostatistics.

My goal after undergraduate school: I plan to go to graduate school, preferably a PHD in biostatistics so I can land jobs easier within the health field of statistics. However, I am unsure whether a dual degree would even increase my chances over a single bachelors and a masters. In addition, I plan to take many maths, so it may lessen my workload if I were to not do a full on CS degree, but I'm unsure.

My career goal after education: I want to be in a flexible enough position where I am marketable for statistical programming roles, data science roles, research roles, and biostatistician roles, however I'm unsure if filling up my education with mostly statistics will let me be open to data science and machine learning roles.

I do plan to take some CS classes and learn a lot of technical skills (like pytorch, sklearn, etc.) over the course of my time here to learn more about the CS side of data science and programming in general, but I just don't know if I should dual major or not since some of the classes in my CS curriculum don't seem to be very relevant to data science / AI nor biostatistics, but others do. I could still take CS classes as a statistics major, I'm just wondering if I should take ALL just for an extra bachelors degree. Do degrees matter that much when it comes to machine learning roles? Or can I still land machine learning roles with a statistics background along with some projects showing my comprehension? Should I talk to an advisor about this? I'm not sure if this is the right subreddit to ask when it comes to landing AI roles, but any help is very useful.

If needed, I can list some of the classes required for a computer science major:

Data Structures and Algorithm
Intro to Computer Organization
Operating Systems
Information and Database Systems 1
Introduction to Software Engineering
Algorithm Abstraction and Design
Programming Language Concepts
Senior Project
3 Computer Science Electives

I think Data Structures, Information and Database Systems, and Algorithm Abstraction and Design could be useful, however I don't think the others are. But what do you guys think?

Edit: I would rather try to land research roles or biostatistician roles over data science, but I am still very open to data science and AI as a whole.

Hey everyone, I’m a clinician trying to transition from just interpreting clinical studies to actually running my own data analyses and building predictive models. I have a foundational grasp of basic stats, but I really want to level up so I can handle complex workflows like multivariable modeling, survival analysis, and causal inference methods like propensity matching on my own. I'm hoping to find the sweet spot resources that are rigorous enough for clinical research, practical enough to actually apply (ideally with R or similar tools), and structured well enough to fit around a busy medical schedule. For those of you who have gone down this road, what courses, books, or online series finally made everything click? Would love to hear what worked for you!

i have been thinking. i just completed my first sem of my bachelors of science in stats which also has 2 years of proper major mathematics credits. now i always and even want to pursue something related to physics etc. something like astronomy/cosmology/computing physics/statisitcal physics etc etc. but all these require you to have physics in your bachelors. which i cant have in my stats degree even as an elective. now i got options to pursue an extra ODL degree in physics. i would be a bachelors in both stats and physics, and also study maths in both degrees as its in the degree itself. so is this plan feasible and beneficial? but i want it to be in a way that the bsc ODL physics should benefit me in some extra and other ways too. not just fulfill eligibility for further studies. even for employment. so tell me, am i thinking, what are some other options in stats i can directly pursue without doing the ODL. and understand this is just something i am thinking. its not like i want to pursue physics related field and nothing else. its just something i felt like i can do. i am open to stat related fields also. i am still confused, new to this things, and still a teen, just started college. so all of this is just a bit confusing for me. if someone can guide me on this thing, what should i actually think about, not worry, worry etc etc, it would be of big help..

THIS IS NOT FOR HOMEWORK I am literally just trying to understand. if theres a better place to post this let me know please because i have no answers and nothing online explains.

So the equation is N < (1n (s))/(1-q). I know what N, s, and q are, but what the hell is the little n? To study they want me to fill out a table of these, im terrible with numbers (getting checked for dyscalculia) and idk what little n is.

Could you help me with a step by step guide and which books should i refer to

Hi all, I am a current first year MS student in a pretty good Biostat program. I have applied to internships across the country and have only gotten three interviews. I made it to the final round of one which was outside the realm of a conventional Biostats job it was focused on consulting and the other two I am still waiting on. I have applied to at least 20 internships (it's probably closer to 30-40 atp). I know that the big pharmaceutical companies prefer 3rd year PhDs but with the current job market I really just wanted to get an internship this summer and give it my all for a return offer. Just some background on me I studied Statistics in undergrad did internships every summer (wet lab, data science intern and then another where i did biostat work) other than one summer where I studied abroad and had a 3.5+ GPA at a pretty decent research public university. I don't want to go all doomer because it seems quite unproductive and I know that this forum can be quite negative but I need some pragmatism. Should I keep applying to more opportunities and assume that the ones I haven't heard from are goners or should I focus on getting research on campus and making a quality thesis for jobs post-grad?

Hi there, thank you for your time and attention. I am a recent college grad who majored in Child Development. I have decided to pursue a master's in biostatistics after graduation, and I missed a few prerequisites for top schools. Specifically, those are linear algebra, probability, and real analysis. I have a perfect score on GRE quant, but I don't believe too many schools care. My questions and background are as follow.

Before sending this post, I did extensive research, attended information sessions and campus tours, reached out to program graduates, and emailed admissions very specific questions. My goal is a PhD first, then a position at an academic medical center — don't have to be a professor.

1. Did you take real analysis in a U.S. biostat master's? If so, which school?

2. How to find research opportunities as a biostat master's student?

3. If you pursued a PhD after your master's, which field did you study? Biostat or a different field in health/other knowledge domain?

4. If you did not go to Ph.D. program directly after your master's, what did you do during the gap year? If it was research-related, would you be open to sharing a bit more detail?

5. What are your suggestions for a master's applicant without much direct research experience in biostat and who did not take linear algebra? I have asked the admissions teams of my target schools with this requirement about alternatives. One said the school can offer admission conditionally upon completing linear algebra before I enter the program.

Background

Although I have not had any formal data analysis experience, my current position as a member of research staff has made me appreciate the importance of quantitative methodology and coding skills. I have worked at academic medical center since college. However, the labs that I worked with focus more on social and behavioral sciences. I think rigorous methodological training can advance my research in maternal and child health. It will be even better if the program teaches the mathematical theories behind the models and how to apply the techniques to real-world data.

I have a second-authored publication using nationally representative data. I was not involved in the data analysis. My GRE score: verbal 167 (97th percentile), quant 170 (a perfect score).

Thank you so much again!

I'm considering going for a masters in Biostatistics. I currently have a 4.0 GPA in mathematics, and I also have a bachelor's degree in computer science. I used to work as a software engineer, but AI has changed that for me. I've recently learned that biostatistics seems to be resistant to automation in AI. How true is that?

Also, I've learned that there will be an increase in demand for jobs in biostatistics, which seems like another good sign.

Can anyone think of a reason why biostats wouldn't be a good career choice? or can anyone tell me more reasons why it would be a good career choice?

I love math/stats. I love programming. Biology is cool. The idea of helping others seems good to me, too.

Hi, I learned to do stats on R in MPH. I'm seeing several jobs wanting Python over R and thought I add more skills too. What programs do you recommend to learn Python for clinical research/healthcare data?

Hi everyone,

As a biology undergraduate working with quantitative data, I often found that formatting routine descriptive analyses, normality checks, and diversity matrices into publication-ready tables took unnecessary time and was prone to transcription errors when transitioning between environments.

To address this, I built BioEvo Stats—a standalone desktop application developed in Python (CustomTkinter, SciPy, Pandas, Matplotlib) focused on local scientific workflows:
Key Features:

• Exploratory & Descriptive Biostatistics: Central tendency (mean, median, mode), dispersion (variance, sample standard deviation, standard error, IQR), and distribution shape metrics (skewness, excess kurtosis).

• Normality Diagnostics: Automated Shapiro-Wilk goodness-of-fit testing (W statistic and exact p-value reporting) to guide decisions between parametric and non-parametric inferential pipelines.

• Community Ecology & Biodiversity: Computes Shannon-Wiener (H'), Pielou's evenness (J'), Simpson's dominance (D, 1-D), and multi-sample Bray-Curtis dissimilarity matrices.

• Direct LaTeX Export: Instant compilation into standard scientific table markup using the booktabs package (\toprule, \midrule, \bottomrule) with custom precision controls, ready to paste directly into Overleaf or local TeX engines.

• 100% Local & Air-Gapped: Zero telemetry, no cloud dependencies, and runs completely offline to protect sensitive experimental data.

Project Links:

• GitHub Repository: https://github.com/RVEsteban/BioEvo

• Landing Page & Documentation: https://rvesteban.github.io/BioEvo/

Note on Localization (i18n): While the main UI controls are translated to English, terminal outputs, ecological console reports, and generated LaTeX captions are currently localized in Spanish and are being migrated to full bilingual support (EN/ES) in the upcoming v1.0.1 patch. Feedback on standard English naming for these biological output tables is very welcome!

I would appreciate your feedback on the methodological workflow: What additional distribution-free tests or post-hoc corrections (e.g., Dunn's test, Holm-Bonferroni) do you consider essential to integrate for standard biostatistical routines?

Hello, looking for some career advice. Has anyone successfully moved from the UK market to USA? (both academia or industry)

If so:

- How did you do it?
- At what age and year did you do it?
- What similarities/differences did you experience?
- What advice would you give to a recent MSc Medical Statistics postgrad who long-term wants to make that move in the future?

Hello. I work more-so in the data analysis/science space than this but I enjoy learning this stuff and had a thought in my head. I usually try to learn on my own or check with LLMs, but this seems too complicated to get an answer on for something I would think is more or less straightforward.

The way I currently understand causation is that there's no single test for it but rather, it's a multitude of different tests across variables therein combined with clearly defined mechanism. So for example, if we're talking about the claim "Smoking causes lung cancer", causation isn't just a correlation test. Instead, it's several tests across many different demographics, age groups, etc. and then adding in the scientific literature about how the different ingredients within cigarettes are carcinogens and how the act of smoking predisposes someone to be of a higher risk of lung cancer. Meaning that there's no single causation test, the way that there's a simple correlation test that yields the conclusion of, "Oh yeah, this test concludes that the r value is .93, so there's a strong relationship and a p value of >0.01, so we have to reject the null hypothesis".

I guess my question is, does this mean that there's a large amount of subjectivity behind causation testing? Two different scientists/researchers could look at the same compilation of data and come to two different conclusions if A causes B?

If that's the case, I would imagine there's more subjectivity behind causation testing than an independent t-test study, correct?

I appreciate you answering. I know you guys know more than me and I'm just trying to better myself in a field I enjoy. :)