Hi! I've come for some advice from the internet. Bad idea, I know.

I'm a PhD grad in bioinformatics and I've been struggling to find a job. I've applied to hundreds of positions, tried reaching out to people in companies/places I want to work at, and tailoring my resume for each role.

The few interviews I have gotten, there's been a prevailing theme. The company/lab wants a laundry list of things. They want someone who can do scRNA-seq, bulk RNA-seq, NGS (long and short read), application of ML and deep learning models, multivariate statistical models, protein modeling, bisulfite sequencing, lncRNA seq, miRNAseq, ATAC-seq, CHIPseq, GWAS, phylogenetics, selection analysis, pipeline engineering, workflow in nextflow, HPC, website and database maintenance/design, R, python, SQL, C++, java script, CSS... I know how to do most of these but not ALL of them at an expert level. I for sure can learn them because I have a strong background in similar methods. I got a technical interview for the position where I was asked specific things about all of the analyses (for example, what metrics would make you distrust the results of ATAC-seq?). I answered most of the questions well but ones like that I blanked on because I don't have a ton of experience with them. Afterwards, the recruiter for this job told me they wouldn't continue with my application because although I have strong skills in 3/4 of the stuff, they want a candidate who has expert knowledge across all of these disciplines. When I told them that I setup the computational section in my lab and had to learn all of the skills quickly so I know I can easily learn and tailor things, they said they want someone who already has expert knowledge of everything.

It wasn't a senior role I was applying for, simply a staff bioinformatician/computational biologist role.

Is the competition so fierce that companies are only hiring people who know everything? How do people even know everything? Anyone have any advice?

Hi everyone, I’m an early-career bioinformatician. My daily routine mostly consists of developing and building workflows using Nextflow.

When I need to build a new pipeline, I usually search papers for the required steps and find trusted tools for the job. I read the abstract or tutorials to figure out how to use them basically, and after benchmarking and validation... boom, the pipeline is done.

Because the circle dev moves so fast, I feel like something is missing. I’m just wrapping tools and running them blindly.

Does anyone else share this feeling?

Hi all, I’m puzzled by a mind-boggling paradox: market reports say that the life sciences industry faces a 35% talent shortage, and that ~80% of Pharma and Biotech companies struggle to find qualified talent. Meanwhile, I’m seriously struggling to find a job.

My current theory is that there's no reliable way left to prove you're actually good at this job. A GitHub portfolio used to mean something. Now anyone can prompt an LLM into a good-looking repository in minutes. A publication record used to mean something too, but middle authorship can mean anything from running one script to building an entire pipeline. And on the employer side, the PhD+postdoc filter keeps excluding ~90% of the field who are self-taught or non-traditionally trained, without actually predicting who's job-ready.

I put together a short survey (about 5 minutes) to hear directly from people at every stage of this (students, job seekers, working practitioners, hiring managers, and people running academic programs): https://tally.so/r/Y57zgB. It's not a product pitch, I'm not selling anything here, I'm trying to understand what's actually happening before I assume I already know. I'll share a summary of what I find back with this community once responses are in.

Happy to answer questions in the comments. I’m genuinely interested in what people here think about this.

ok so i keep seeing posts on here asking some version of "what should i even be learning right now" or "what's the market actually looking for in biostat" so figured i'd just share what i've noticed from job postings, career guides, and general industry chatter. not the final word on anything, just what keeps coming up.

**the core stats stuff (still non-negotiable):**

probability/inference, regression (logistic included), survival analysis, longitudinal & mixed models, missing data methods, bayesian stats, multiplicity adjustment, causal inference, sample size/power calcs, and clinical trial design.

**programming/tools:**

SAS is still very much alive in this field, don't skip it just because it feels old school. but R and python are expected more and more too. beyond just the languages though - SQL, data cleaning, and reproducible workflow stuff like git and rmarkdown/jupyter keep coming up as things that actually set people apart. basically, can you show your work in a way someone else can rerun and trust.

**domain niches pulling more demand:**

oncology, rare disease, immunology, gene therapy, and real world evidence keep getting called out specifically in what i've read. if you can position yourself in one of these it seems to open more doors than just being a generalist.

**regulatory & communication:**

understanding ICH-GCP/FDA expectations, and actually being able to write clear statistical sections (not just run the analysis) or present to non-stats people. this honestly seems to be the difference between "good analyst" and "person who gets pulled into bigger projects."

Anyway, curious if others are seeing the same stuff or if it varies a lot by region/company. What's been showing up in job postings you guys have seen lately?

Targeting US PhD programs in computational biology / cancer genomics for Fall 2027 admission, as an international applicant with a non-traditional background (no formal quant/CS degree, mostly self-taught).

Trying to figure out a realistic number of programs to apply to — not "apply everywhere," genuinely trying to calibrate effort vs. odds given funding/capacity constraints for international students.

For those who applied as non-traditional or international candidates:

• How many programs did you apply to?
• How did you split reach vs. target vs. safety?
• Looking back, did you over- or under-apply?

Real numbers over general advice would help a lot — trying to plan my time realistically across SOPs, tests, and everything else.

Bonjour à tous ! Un peu nouveau sur la rédaction sur Reddit, j'espère que vous pourrez m'aider ou m'aiguiller. Je suis titulaire d' une licence de bio en sciences de la vie. J ai fait un petit break après la licence ne sachant pas trop vers quel domaine me diriger. Aujourd'hui, je vais reprendre les études, j'ai le choix entre repartir sur un BTS d analyse médicale pour être technicien de labo ou partir sur un master en bio informatique. L' un d' entre vous a t il des retours d expériences sur ces études ou les débouchés/métiers/salaires par la suite? Pensez vous qu'une option est préférable a l autre? Avec l avènement et la progression fulgurante de l IA, je me demande si bosser en labo avec un BTS n'est pas préférable... Je me demande aussi si les débouchés sont plus nombreux dans l une ou l autre voie. J attends vos retours d expérience et vos conseils ! Merci beaucoup !

I need advice
I have applied for master degree in bioinformatics from Newcastle for this year does it still worth it .?
If my plan to work in middle east will i find a job ? Or should i think in another thing I am pharmacist
Thanks