So, you’re considering pursuing a career in Statistics but you’re not quite sure. You may be at Uni studying a BSc in Mathematics or a related field, or you may be considering a change in career. How do you get from where you are now to being a Statistician in the medical industry? What are your next steps?

This episode will cover what to start thinking about now, where you can get further advice on MSc courses and funding and where you can search for jobs.

Do you often find your workday spiraling out of control?
Do you want to be more productive than you yesterday?
Do you want to know how to improve your skill set and work performance?
Do you start each day with a plan, but soon become distracted and procrastinating?

You are not alone. Sometimes, it’s easier to wait for someone else to tell you what things you should work on rather than to assess yourself.

Focusing on continually improving your skill set will boost your confidence and help you become the best version of yourself.

In today's episode, Benjamin and I discuss what you need to know to increase productivity.

Listen now to be an effective Statistician!

How do you know if you are an introvert?
Is it possible to lead successfully even as an introvert?
Do you know how to be successful being an introvert?

People who are introverted tend to be inward turning, or focused more on internal thoughts, feelings and moods rather than seeking out external stimulation. They tend to be more quiet, reserved, and introspective. Unlike extroverts who gain energy from social interaction, introverts have to expend energy in social situations.

Statistics isn’t traditionally known to be a trendy subject, but here we are saving lives around the world by developing new and mighty medicines. You’ve probably heard by now that data is basically gold, and fortunately we statisticians work with data.
Now that’s a lot of power we hold, so let’s put it to good use!

- What kind of companies are out there?
- Statistics is needed to produce revolutionary new medicines - yes, really!
- Will I be at a desk all day, on my own, in a windowless room?
- What’s a typical day? Excite me..
- Learn about different diseases
- Not just a stats nerd - the other skills that are VERY important

Do you know why network meta-analyses (NMA) important?
Do you wonder what steps are included in NMAs?

Network meta-analysis is a very important field, especially for multiple treatment options where a direct comparison is impossible without using this systematic review and analysis.

Do you really know how to deal with conflict?

Conflict is one of the biggest topics of stress at work. Managing conflict gives a huge drive to our success and satisfaction at work but in reality, it's really a challenge to manage it.

Today, we give you the recording of a webinar with Gary on how to effectively resolve conflict without giving in and you'll learn about these points:

How do we develop new medicines to treat nasty diseases? It all starts by discovering a novel target molecule in the lab. Fast forward 12 years and an average expenditure of 1.5 billion dollars we hopefully get the thumbs up from health authority bodies to license the medicine for public use.
So we skipped a lot of important steps there. The journey for developing new medicines is extremely lengthy and complicated. Fortunately, you can listen to this podcast and know all the important stuff in 20 minutes!
Statisticians are involved in all aspects of drug development. Having knowledge of the process from start to finish is important, as it will help you understand why statistics is an essential and relied upon toolkit in this industry.
- Quick overview of the drug development process
- Where and why statisticians add value in the process
- Key stakeholder interactions - who do statisticians collaborate with?

What are the bigger trends in the biopharmaceutical industry, which will have an impact for statisticians?
. . . in terms of technology changes?
. . . in terms of regulatory changes?
. . . in terms of political/society changes?
What can statisticians do to prepare for these changes?

Why is text mining so important?
What are the typical challenges and research questions in this area?
Are there any ones typical for medical/pharmaceutical research?
What data sources could we tap into?
What are some foundational concepts in natural language processing?