Losing a job is never an easy experience. It can be especially challenging when it happens unexpectedly. Unfortunately, layoffs are a reality for many people in any stage of their career. Even if an individual has been working at the same company for years, they are surely not be immune to layoff decisions. That's why it's crucial to prepare in advance and build a safety net for job security.

In this episode, I discuss some tips on creating a Plan B strategy that can help you to stay employable.

 Here are some points and strategies:

Podcasts have become a popular form of media for sharing information, telling stories, and building an audience. But have you ever wondered what goes on behind the scenes of creating a successful podcast?

Today, I have the pleasure of sitting down with Reine Escalona and Kacey Tunac, the podcast production and management team called VVS, who works alongside this successful podcast - The Effective Statistician.

As you already know, I started this podcast six years ago and have recorded over 300 episodes with the help of  VVS. The podcast has seen steady growth, now with 14,000+ subscribers and over 200,000 downloads. I achieved this growth through the use of effective marketing strategies, consistent content creation, and excellent audience engagement. And of course with my outstanding team at VVS.

In this episode, we explore the tools, skills, and strategies needed to start a podcast and grow its audience from the ground up.

We also discuss in detail the following points:

You have to be extroverted to be a leader. 

Only if you have employees are you a leader.

Either you have it or you don't. Leaders have a natural gift, you can't learn it.

Those are three typical myths about leaders. In today's episode, I talk with Gary about these three false beliefs. We point out why they're false and give some counterexamples.

We also discuss why they limit your development and make you a victim, so to speak.

As statisticians, we are often introverted. We can use this to our advantage. We can take leadership, in our cross functional teams. And we can learn these leadership skills as well.

All good leaders are always learning.

Listen to the latest episode and share it with your colleagues.

Personal growth is an integral part of every individual’s life. It is a continuous process of self-improvement, learning, and self-discovery. As a statistician or statistician enthusiast, you should always be on a quest for personal and professional development. The journey towards growth is unique, and understanding yourself is a critical step towards achieving your potential.

In this 300th episode, I discuss the three things about yourself that could make a significant difference in your career and personal development. While many self-help resources exist, this episode provides practical insights into understanding yourself better.

I talk about the three important things you need to develop to achieve personal and professional growth:

Transcript

[00:00:00] Alexander: Welcome to a new episode of The Effective Statistician, and today I'm super excited to talk with Sheila about her project that I think is a really important part in the overall puzzle of developing new medicines, new diagnostics, and all kind of other things in our world of healthcare. Welcome to the show, Sheila.
[00:00:30] Sheila: Thanks Alexander. So happy to be here and thank you for the kind words of intro.
[00:00:35] Alexander: Yeah. Maybe for those who don't know you, you can shortly introduce yourself and what you brought into LifeSciHub.
[00:00:44] Sheila: Sure. I'm Sheila Mahoney-Jewels, I've been in pharmaceutical research and development for nearly 25 years. I originally started in regulatory operations on the sponsor side. I moved over to the vendor side. [00:01:00] In 2014, I became an independent consultant. I have been I've evolved beyond regulatory. I'm now a, I consider myself a cross functionalist, having worked directly in projects in almost every domain area of expertise.
Not the lab bench and not the sales force of a pharmaceutical company, but almost everything in between. And I am now focused. In addition to continuing to be an independent consultant myself, directly working on r and d projects, I've created LifeSciHub, which is a new approach to finding non fulltime employee talent.
[00:01:45] Alexander: Yeah. And we'll get into this now with this episode today. I hear so many kind of people talking, oh, it's so hard to find talented people. It's so hard to find [00:02:00] experts. Do you agree with that kind of proposition?
[00:02:04] Sheila: Yes and no. I see an abundance of talent in the LifeSciHub community and network in terms of there are many independent small business of one experts. On the drug sponsor side, I do hear a lot of challenges in finding talent that, as a matter of fact, it's I've been surprised investors that I've spoken to have said that talent, they're very interested in any solutions that are addressing r and d talent. Because access to talent is actually a significant risk to their portfolio companies.
So they've invested in these small biotechs and they are seeing the struggles and the, I'm hearing this from more than one investor. They've actually reached out to LifeSciHub because they're actively seeking talent solutions. So yeah, I think [00:03:00] it's real.
[00:03:01] Alexander: Yeah. Yeah. And these interesting discrepancy. On the one hand you have companies that struggle to get really high quality experts. We are not talking about the people that, come fresh out of university but people that have a deep expertise that are fundamental to the success of smaller companies but also bigger companies.
And on the other hand, we have these. Individuals or these peoples that work, let's say in small companies, let's say they just have a partner or it's a, the three of them, they have formed a new company. Lots of these. So the question is, first for me, why do these, very talented people? Why don't they go for working for big company? Why don't say hire sponsor one of these, big full service providers. I would [00:04:00] say, why don't say Itk?
[00:04:02] Sheila: Could you repeat the question? I apologize.
[00:04:04] Alexander: So why do these very talented individuals, these people, with a deep expertise in their area of specialty, Why don't they work for a big pharma company or a big full service provider?
[00:04:22] Sheila: Ah, I see. So in other words, why are they independent? Instead of working full-time or working for a large pharma. There's a few different reasons. Work-life balance. As a matter of fact, LifeSciHub has conducted a number of studies on this population and or a new talent pool, quote unquote as it could be called very gig economy talent. And work-life balance and flexibility, choice of projects, all of these things come up an awful lot. There's a really great story that really speaks to, I think, a primary reason why

In part 1 of this series, we explored the perspectives on commercializing statistical inventions, leveraging external resources, collaboration, and setting up a community of statistical methodology leaders. Innovation is essential for advancing drug development, and statisticians play a vital role in driving statistical innovation. However, to overcome the barriers to innovation, statisticians need to work together, share information openly, and cultivate a culture of curiosity and experimentation.

In this part 2, Mouna, Kaspar, and I share our insights on the strategies for innovation in statistics for drug development. 

We also talk about these specific points:

Statistical innovation is vital in the pharmaceutical industry as it drives evidence-based decisions and brings value to patients. However, it requires a delicate balance between invention and commercialization to achieve success.

I, together with Mouna Akacha and Kaspar Rufibach - two leaders of statistical innovation groups in big pharma companies, share their insights on the barriers and strategic planning in driving statistical innovation. In this first part of the episode, we will discuss their perspectives on commercializing statistical inventions, leveraging external resources, collaboration, and setting up a community of statistical methodology leaders.

Join us while we also discuss the following points:

In any industry, staying competitive and relevant requires a constant focus on innovation and the ability to communicate effectively with customers. In the pharmaceutical industry, this is particularly true as companies race to develop and market new compounds that can improve patient outcomes.

In this episode, I share insights into the importance of effective data communication for success in this industry. Specifically, I discuss an experience with helping a competitor with their need to communicate data effectively in order to differentiate their compound from the standard of care.

Listen to this episode to learn about the value dossier, innovative data visualizations, and practical tips for improving data visualization skills.

I also discuss more points such as the following:

As a statistician, you're likely already familiar with SAS and the value it brings to data analysis. But in this ever-evolving world of data science, relying on your go-to language might not be enough to stay competitive anymore. The open-source language R is gaining traction within many industries as a powerful tool for analyzing complex datasets.

In this episode, join me while I talk about why learning R can help you stay ahead of the game—and why now is the perfect time to dive into its growing popularity among healthcare specialists and statisticians alike.

 I specifically talk about the following points:

In the world of statistics, information can sometimes be missing in data sets, leading to a challenge in understanding how well treatments may work. Policy estimands are used to understand efficacy based on early treatment decisions. Various approaches, like reference-based imputation and delta adjustment, exist to speculate what may have happened after treatment was discontinued. However, these methods are often inconsistent, and more efficient methods are required.

In this episode, Alberto and I discuss how his new approach can handle different scenarios with missing data and can estimate policy estimands for faster results. As a 26-year veteran of the pharma statistics industry that recently completed his PhD research, Garcia brings a wealth of knowledge and experience to this topic.

So, let's dive into the details of this innovative framework for estimating policy estimands such as the following: