The Best Websites To Apply For Biostatistics Jobs

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time-spend--by-biostatisticians-finding-biostatistics-jobs

Looking for biostatistics jobs for your next career move? You’re in the right place!

In this article I’ll share with you where most biostatisticians go looking to find biostatistics jobs – and where you should be applying to drastically increase your chances!

Where Most People Search For Biostatistics Jobs

General Job Boards

These are the big job boards that everyone has heard of. We’re talking about Indeed, LinkedIn, Glassdoor et al.

These are ok. They have a lot of jobs available, they’re sometimes linked to the company’s profile, and they are updated fairly regularly.

But there’s a downside: spamming, bots and fake job adverts are all too common.

The big problem is that everyone applies for jobs through these job boards. The more applicants, the less likely your resume will stand out. What this means is that you have a lot more competition in getting a callbacks for these jobs.

You’ve probably seen biostatistician jobs that have been posted on LinkedIn in the last day but already has hundreds of applications – demoralising, right?

But Your Resume Will Still Get Looked At, Right?

I’ve got some bad news for you! You may think that if you submit your application in that you’ll still get your resume reviewed. Unfortunately, that’s not the case!

LinkedIn filters out applications for employers. This means that the more applications the job advert receives, the more that will be discarded by LinkedIn before the employer even gets a chance to review them.

In fact, the last company I worked for refused to review applications after they had received an arbitrary number of applications from these job boards – “Applicant number 101 – sorry you may have the resume, but we’re only reviewing the first 100″.

This means there’s a good chance the application you worked so hard on won’t ever be seen.

This is why I advise against applying through these platforms unless you have to.

So what are your other options?

Industry-Specific Job Boards (For Biostatistics Jobs)

These are catered for those in certain industries – in this case life sciences, pharmaceutical or academia.

These job boards may have fewer listings, but they’ll be highly targeted for biostatistics jobs, making them more relevant to your job search.

You will also find a lot opportunities that don’t want to advertise on the big job boards because of the amount of spam and bots that apply through these platforms.

The biggest benefit of applying through these job boards is that they get a lot fewer applications, so you stand a much better chance at getting your application through to the hiring manager.

Here are some industry-specific job boards:

 

Biostatistics Jobs Recruiters/Agencies

Recruiters and agencies can provide unique biostatistics jobs that aren’t offered elsewhere.

Some companies go through recruiters because they have a person with a very specific skillset in mind for the role. If you’ve got some unique experiences and skills mentioned in the job post, this can work out in your favour.

Companies sometimes prefer to go through this route as the recruiter can pre-qualify the candidate by conducting the initial screening interview. The problem with this is that you can end up in a scenario where you’re being interviewed twice – which isn’t ideal.

Recruiters tend to be quick to reach out and also quick to ghost. This can be frustrating if you’ve put effort into your application. The positive is that you will find out pretty quickly if they’re interested in your resume or not.

Here are some recruiting agencies offering biostatistics jobs:

Where Should I Be Applying?

So, you’ve found a great job you want to apply for on a job board or recruiters website, you should click apply right?

Not so fast!

If you apply through these channels, you will be applying through an intermediary who holds all the control in whether the employer actually receives your application.

So where should you apply?

Company Career Pages

When you apply through company career pages, you stand a much better chance of your application actually being read by a hiring manager.

This is because most people do not apply through this channel.

We as humans tend to be lazy so we go to job boards that aggregate all these jobs – it’s less effort.

But these will filter out your application, reducing your chances of getting the job. I can’t tell you how many times I’ve applied through job boards and recruiters – only to be ghosted by them. But when I applied directly through company websites, I received callbacks for interviews.

There is no harm applying through a job board if it’s a quick apply where you only send your resume in – but don’t expect results.

Applying On The Company Website

So how do you find the job on the company website?

Sometimes the job advert will name the company, sometimes it won’t. Recruiters and agencies often don’t provide the company name because they don’t want you applying directly and losing their commission.

If the job advert contains the company name – it’s simple. You find the company website’s career page and look for the job advert – click apply.

If it doesn’t contain the company name – then googling the introductory paragraph can find the job on the company’s career page in a lot of cases.

But What if the Job Isn’t on Their Company Career Page?

If the advertised biostatistics job isn’t on their company career page, you need to get as close to the source as possible. This means apply in the places where you’re most likely to have your resume read by the hiring manager. I’d order this as follows:

  1. Company’s LinkedIn page – Best chance for visibility.
  2. Industry-specific job boards – More focused and fewer applicants.
  3. General Job boards/Recruiters & agencies – Only if you must.

You can even follow this up with an email/LinkedIn message to the hiring manager if you know who this is.

Conclusion

Take a look at the current biostatistics jobs adverts from some of the above websites. If you find one that interests you – apply through the company career page, and you stand a much better chance of being noticed. Good luck!

 

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How To Learn R For Biostatistics

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learning-r-for-biostatistics-learning-curve

So, you’ve started an MS and want to learn R for biostatistics? Exciting times! But if you’re struggling, you’re not alone.

Many students really struggle to get to grips with R during their Biostatistics MS (about 70% of my masters cohort).

This is a big problem!

A large proportion of Biostatistician jobs will require you to be proficient in R, so if you don’t know the difference between a vector and a DataFrame- you’re going to be limiting your chances in the job market.

In this post I’ll talk about why learning R can feel so difficult and how you can master it effectively.

Why Are You Struggling To Learn R For Biostatistics?

You’re struggling to learn R in your biostatistics MS for two reasons:

a) You’re book-smart, but not as practically inclined.

  • You have strong theoretical background but haven’t had much hands-on coding experience.

b) Your university teaches R like theory course.

  • Endless PDFs, dry lectures and minimal practical application.

But biostatistics is a highly practical field, you need hands-on experience with R is key to build confidence and competence. You won’t get that if you’re learning R the wrong way!

My Experience Learning R For Biostatistics

I made many mistakes when I first tried to learn R. Here are just some of them:

  • Reading textbooks about R instead of coding in R
  • Trying to memorise code instead of understanding how it works
  • Copy and pasting code from others without grasping the logic
  • Jumping into projects before learning the basics

Looking back, I wish I could have saved hours of wasted time by learning R differently.

I can’t go back, but at least I can help you avoid my mistakes and learn R the right way.

How To Learn R For Biostatistics (The Right Way)

In order to learn R for biostatistics applications – you need to learn it in the right order.

The steps you need to take to learn R are as follows:

  1. Learn syntax and basics
  2. Solve basic problems
  3. Follow along with real projects
  4. Start your own project

You might want to rush these steps or skip a step. This will only lead to frustration!

It’s like learning any language—if you try to write complex sentences before you know the basic words, you’ll struggle.

Similarly, in R, if you try to jump straight into complex modeling without understanding the basics of data manipulation, you’ll likely feel lost.

Step 1: Learn Syntax and Basics

Before you jump into advanced techniques, make sure you understand the syntax and basics.

This includes topics such as variables, data structures, for loops and functions.

How to do this:

  • Read or watch a tutorial, but don’t passively consume it – code along in your R console.
  • Focus on understanding, not memorizing. In the real world, no one is expecting you to remember everything.

Once you understand the code, move on to the next lesson.

Here are some free resources for you to follow:

This step may sometimes feel a bit frustrating, but it’s essential.

Before moving to the next step, make sure that you’ve covered all the concepts in one of the above resources.

Step 2: Solve Basic Problems

Now you’ve got to grips with the basics, it’s time for you to apply what you’ve learned.

You’re not quite ready to write your own project yet, but what you can do is solve basic problems.

I recommend you complete a worksheet problems or lab exercises.

Why is this the best way to practice?

  • Completing exercises will give you a chance to put your new found R skills into action.
  • Comparing your code against the solutions will provide you with immediate feedback, which will help you improve.
  • Solving small exercises gives you a sense of accomplishment that you’re actually learning R.

This step can be daunting – you may feel you don’t know enough or you will get stuck.

Don’t worry! There will be exercises where you haven’t covered the background yet. This is by design – this is what real life biostatisticians encounter all the time.

If you get stuck – go back to the resources above and look for answers, google to help you remember syntax or data types.

Remember, this isn’t cheating – this is exactly what many biostatisticians do daily in our jobs to help rejog our memory.

One thing I don’t recommend you doing is to find the answer using google or chatGPT (it can be wrong). You won’t be learning – you will be copying.

You can use these resources to answer questions like “how do I run a for loop in R” but not “A scientist needs to experiment upon 4 conditions, 5 times each. Generate a vector of length 20, representing these conditions in R.”

Notice how the first gives you tools to solve the problem, the second answers the problem for you! Here are a some free exercise worksheets with solutions:

If you’ve managed to answer all the questions in one of the worksheets – feel free to move onto the next step.

Step 3: Follow Along with Real Projects

Now that you’ve built a foundation, it’s time to see how R is used in real biostatistics projects.

Finding industry projects or someone’s PhD code might prove difficult at this step but you can still follow along with real projects.

The best way to find these projects is with kaggle.

Kaggle is a community for biostatisticians and data scientists to share, test, and stay up-to-date on all the newest techniques. There is a huge repository of community-published projects. The best thing is that they are awarded with upvotes if they are useful. This is where you’re going to discover a project to follow along with.

Your goal:

  • Read through someone else’s project code.
  • Try to understand their code – don’t just skim it.
  • As yourself questions – what is the code doing? Why have they used a specific function?

ChatGPT can be used at this step to clarify anything you don’t understand – but try to understand it yourself first.

Here are some of the best biostatistics projects in R to follow along with:

If you’re comfortable with what the code is doing, move on to the next step.

Step 4: Start Your Own Project

Now for the exciting part – to start your own project.

To start, you can try to run a similar analysis from the project in the last section but run it for a different dataset.

Don’t look at the analysis others have done on the dataset you’ve chosen to avoid any preconceptions about what analysis to do or what features the dataset may have.

If you feel confident enough, feel free to design the analysis yourself. Here are some sections you can use to guide you in your analysis:

  • Exploratory Data Analysis (EDA)
  • Data Cleaning (if needed)
  • Data Transformation
  • Model Building
  • Model Evaluation
  • Model Interpretation
  • Model Visualization

If you get stuck – the R and Biostatistics communities are incredibly active and willing to help. Don’t hesitate to ask questions on Stack Overflow and community subreddits (r/biostatistics, r/RLanguage, r/rstats).

Here are some biostatistics datasets you can use:

Conclusion

Follow these steps to learn R for biostatistics! By following these steps, you’ll not only improve your skills for your MS but also enhance your job prospects by building strong technical skills that employers value (remember to add the project to your resume).

 

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Are Biostatisticians Safe From The Threat of AI?

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The End of Biostatisticians?

The era of AI is upon us. Naturally, this means a lot of people are questioning whether their jobs will be automated by AI. Biostatisticians are particularly worrisome when it comes to this matter. This probably has something to do with our analytical, overthinking brains that makes us so inclined to this field in the first place. Personally, I don’t think we need to be so worried.

Reasons Why Biostatisticians Are Safe

Here are some 10 reasons why I am not worried about AI taking biostatistician jobs, and you shouldn’t be either.

1. AI would be a threat to most jobs, not just biostatistics – If biostatisticians can be replaced – so can most other jobs, so at least we’ll all be unemployed. Adaptability is key in any field.

2. AI is a tool, not a replacement – I believe biostatisticians who integrate AI will be in demand. It will be used as a tool in your work, but it will not replace your need to use the tool.

3. We haven’t reached sentience (yet) – Human oversight in healthcare and statistics remains crucial. Until we have an iRobot situation, we shouldn’t be worrying so much – you’ll still need to oversee what AI is doing.

4. Healthcare and pharma are highly regulated industries – AI adoption is slower due to ethical, legal, and compliance concerns. The general public has a scepticism to “big pharma” as it is – try telling them that AI is in control.

5. AI-generated results will still require human expertise – AI can automate tasks, but statistical reasoning and validation are human-driven. Ask biostatisticians if chatGPT is even close to doing all their work now.

6. Pharmaceutical companies rely on regulatory approval – AI can’t yet handle FDA/EMA compliance, clinical trial design, or medical ethics alone. Regulatory bodies will not accept AI doing this work.

7. AI bias and errors make human verification essential – AI models can produce misleading results if not checked by experts. We’ve all had chatGPT make a mistake when you’ve asked it a question, bias and errors will happen.

8. Developing AI for biostatistics requires biostatisticians – AI models will depend on biostatisticians to develop, maintain and improve their functionality. Even if AI takes over, you will still be needed to maintain our AI overlords.

9. Public trust in AI-driven healthcare is low – Decision-making in medicine still requires human professionals to ensure accountability. Just like you wouldn’t fully trust chatGPT diagnosing you with a disease – trusting it to design studies and analyze complex data is fraught with doubt.

10. New AI-driven job roles will emerge – Biostatisticians who upskill in AI/ML will have opportunities in adjacent fields. Health data science and medical AI development will likely grow – this allows the savvy biostatistician to have some flexibility in the jobs they apply for.

Not convinced?

Look up recent job postings for biostatistician jobs. Notice that how there are still jobs available. Take a look at how many list AI/ML skills as requirements. This is a great sign that the field is adapting to AI rather than being replaced by it, and it shows that there is still an ongoing demand for biostatisticians.  

 

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A Simple Biostatistics Resume Tip That Will Get You Noticed

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job-search-biostatistics-resume

One interview after 3 months? Don’t worry! Keep your biostatistics resume as is. As you can see, by the end of the year you’ll be inundated with interviews…

The Harsh Truth About Biostatistics Resumes

It’s no secret that the biostatistics job market can be a tough one. If you’ve spent time on the r/biostatistics subreddit, you’ve probably seen job search Sankey diagrams that paint a harsh picture.

For example, one Reddit user applied to 300 jobs before landing a single job offer. In their words:

“TLDR; it’s rough out there folks.”

Or as another user, who applied for 330 jobs before they got a job offer, shared:

“I’ve been on the job hunt since early 2023 somewhat casually with some more serious portions of the search. I sent out a bunch of apps for jobs I probably wasn’t qualified for so that inflated the number, but it was brutal.” 

Wow, that’s over a year and a half looking for a job!

The biggest hurdle in your job search? The application stage. In fact, over 90% of these applications are either ghosted or rejected*. This is an extremely high number when you consider how much time and effort you put into your job search, that is essentially, wasted.

(*93.65% based on the posts quoted above.)

So, how do you increase your chances of getting interviews? By reducing the amount of applications that get ghosted/rejected at this stage. Close this gap, then you stand a better chance at actually getting a job.

The good news is that it doesn’t have to be this hard. With the right tweaks, your resume can stand out from the pile.

I know this because I’ve done it. The last time I applied for jobs, I got multiple callbacks and landed a senior role, in a highly competitive remote position – plus a $40,000+ raise.

A mistake a lot of biostatisticians make is to think their resume is just a summary of what you’ve done.

It’s not.

Your resume is a marketing document with one goal in mind – to get you interviews in a highly competitive field. And it needs to be written that way.

Is Your Resume Really a Problem?

You’ve applied to 30 jobs. Zero callbacks. Is your resume the problem? Probably.

A source suggest that you need to send around 10-15 resumes to get one callback. If you’re not hitting this callback rate, you probably need to start making some tweaks to your resume.

I know what you’re thinking, “I’ll just apply to more jobs and keep my current resume. Statistically speaking, I will get a interview eventually”.

I won’t argue with the stats but I will argue that this approach is a huge waste of your time and effort.

Instead, you can significantly increase your chances of getting an interview by just making one simple change to your resume!

Your Current Resume is Boring

Most biostatistics resumes read like job descriptions rather than success stories. 

Here’s an example of a weak bullet point:

“Performed data analysis using R and SAS.”

I will say on behalf of everyone who has to read a resume like this, *yawn*.

This is vague, uninspiring and indistinguishable from every other resume. Hiring managers see hundreds like this.

This is a chance to sell yourself to the recruiter, not regurgitate your last job description or university project.

Fact is, a lot of applicant have the same background on paper when you look at them through this lens.

Every applicant has experience with programming and stats, has done the titanic survival analysis and has a degree.

This does not set you apart from other applicants and will not help you get that interview.

Your resume should sell you, not just list what you did.

The Anatomy of an Interview-Winning Biostatistics Resume

So how do you make your resume stand out from the rest? By transforming your experiences into quantitative, results-orientated actions.

Use this formula:

Action Verb + What You Did + Result (with Numbers)

For example:

Before: “Performed data analysis using R and SAS.”

After: “Analyzed clinical trial data using R and SAS, improving model predictive accuracy by 20%, leading to better patient outcome forecasts.”

Much better! The quantitative result makes it stand out.

You might ask, “What if I don’t have any clear, quantifiable outcomes?”. In that case, add a quantitative action instead.

For example:

Before: “Created statistical analysis plans based on study protocols.”

After: “Developed statistical analysis plans for five high-impact studies, aligning methodologies with trial protocols to enhance research validity.”

Even in academia or non-profits, results matter. All organizations need to save money or generate funding, and your work contributes to that. Showcase it.

Here’s an example:

Before: “Worked with a senior researcher on the analysis of progression time to Alzheimer’s Disease.”

After: “Collaborated with a senior researcher on the analysis of progression time to Alzheimer’s Disease by automating statistical processes to reduce the estimated analysis time from 4 weeks to 2 weeks.”

This highlights efficiency and impact – things every employer values.

Don’t have much work experience? No problem! Use examples of university projects, coursework and personal side-projects.

Why This Works

  1. It shows not just what you did, but why it mattered.

  2. Numbers grab attention. They’re easy to scan and prove real impact.

  3. It differentiates you from other applicants. Your experiences are unique – your job description or university degree is not.

Try it Out For Yourself

Pick one bullet point from your current resume and rewrite it using the formula above. Describe the action you took. Show a quantitative result.

  • If you improved a process, how much faster did it get?
  • If you built a model, how accurate was it?
  • If you worked on a big client project, what was the value of it?

Simple.

Conclusion

That’s it – just one tweak. But it’s powerful. Apply this across your entire resume, and you won’t just “get through the system” – you’ll get noticed.

Most biostatistics resumes read like dry job descriptions. Don’t make the same mistake. Stand out, and land more interviews.

 

Ready to Land More Biostatistics Interviews?

 

Get my free email course that walks you through the exact steps biostatistics professionals are using to get more callbacks, without rewriting their resume 100 times.

  • 🔬 Step-by-step, field-tested advice
    Tried-and-true strategies designed specifically for biostatistics job seekers.
  • ✅ Actionable steps you can apply immediately
    Each email includes one clear step to help you get noticed and get interviews.
  • 📬 1 email per day. Zero fluff. Just results.
    Short, focused lessons that respect your time—and deliver real value.