The field of statistics is rapidly growing, and statisticians play an important role in providing a competitive advantage to companies. However, finding the right job or candidate for a position can be challenging. That's where Clivoffective comes in - a collaboration between Clivolution and The Effective Statistician focused on connecting statisticians with career opportunities.

If you're a statistician or hiring manager in the field, Clivoffective offers a personalized touch that sets it apart. Whether you're looking for a new career opportunity or seeking the right candidate for your company, Clivoffective has resources and expertise that can help make the process smoother and more efficient. 

Join the Clivoffective Network to get exclusive access to networking opportunities and career development resources that can help take your career to the next level.

In this episode, I explore together with Evelyn and André what Clivoffective has to offer and how it benefits both statisticians and hiring managers. We also talk about the following points:

In the field of statistics, we need specific skills necessary for being successfull. Debarshi Dey, currently the head of the Statistics and Programming and Data Management department at Morphosys, has spent the past 13 years in the pharmaceutical industry. With a PhD in statistics from UC Riverside, Debarshi believes that to be a "killer statistician," one must adopt the roles of a detective, lawyer, and storyteller.

Debarshi and I discuss these key characteristics that make a statistician effective.

We also explore these key takeaways from our conversation:

We statisticians have always been important for brining new therapies to patients. We design experiments, analyze and interpret data to provide valuable insights that can help make informed decisions. However, the value of our work increases if we're able to lead others. 

But becoming an inspirational statistician requires more than just technical expertise. As you continue to grow in your career, don't forget to invest in yourself by learning from training programs, books, movies, and other resources that can help you become an inspirational leader.

In this episode, Alun Bedding and I discuss how statisticians can become inspirational leaders and reach a higher level in their careers. We also explore suggestions and give additional insights on how statisticians can become leaders in their field. 

Becoming an inspirational leader as a statistician requires a mindset change from technical proficiency to people skills. Leaders, by inspiring others, can help their and other teams grow and work towards the organization's goal. Building trust is key to becoming an inspirational leader.

Alun and I also discuss these important points in order to level up your career and organisation:

Are you a statistician or data scientist looking to excel in your career and make an impact in the healthcare industry?

Today's episode is inspired by a German podcast that delved into five key reasons for career failure and I fine-tuned this to fit statisticians.  As professionals working with complex projects and tight deadlines, it's important that we recognize these pitfalls before they cause irreparable damage to our success.

Join us as we explore each of these five potential hazards, discuss why they have such potential for harm, and learn from engaging expert advice on how best to prevent or overcome them:

If you're a statistician, data scientist, or programmer looking for quality training opportunities that will help you advance your career, you might want to look into The Effective Statistician Academy. This exclusive academy offers a range of courses that cover essential topics in the field of statistics. From leadership to innovation, knowledge, and excellence, you're sure to find a course that fits your needs and goals.

The Effective Statistician Academy offers four distinct areas of training: Leadership, Innovation, Knowledge, and Excellence. Each area features expert-led courses that are designed to help you gain the skills and knowledge you need to succeed in your career. Whether you're looking to take your leadership skills to the next level or stay up-to-date on the latest innovations in the field, the Academy has you covered.

In this episode, I will discuss more the key features of the Academy, the topics it will cover, the top-notch speakers who have extensive experience and in-depth knowledge of the subject matter, and why you won't have to break the bank to take advantage of this exciting opportunity. 

I also talk about the following points:

What strategies do you use in your work as a statistician to ensure successful outcomes?
How do you set expectations with stakeholders when tackling a project?
Are there any common mistakes statisticians make when setting expectations in their job that you have learned to avoid?
How setting realistic expectations can positively impact your work as a statistician?

As I embark on this journey of discussing expectations, it reminds me of an incident that happened to me recently. During one of my business trips, the coffee I ordered at an airport was quite enjoyable; however upon opening the cup - much to my dismay - only half filled with liquid gold! The size and shape established a certain expectation for what should be inside which created feelings of dissatisfaction due to unfulfilled hopes. It goes without saying that if they would have had appropriately sized cups, then there wouldn't have been any misalignments in regard to meeting expectations. Fascinating how something as small scale such different outcomes can make or break experiences!

As statistical professionals, we frequently encounter situations where our technical expertise is required to communicate complex data insights to a non-statistical audience. Creative data visualizations, tailored to the audience, are an effective tool to help us overcome this communication gap. By presenting information in a clear and understandable manner, we can over-deliver expectations and make a real impact on our work projects.

In this episode, I give more tips on how to set expectations and the following points:

As statisticians, we rely on good quality from programmers and data management. And if you talk to your colleagues, everybody has a story about a poor experience. But the questions are:

How do we collaborate with other departments to create a quality deliverables?
How to strike the right balance between working on processes and managing people for maximum efficiency?
How to we take ownership and drive responsibility to learn from mistakes and not blaming each other?

Tension often arises between statisticians, programmers, and data managers when it comes to delivering quality products in a timely manner.

In this episode, Benjamin and I discuss how you can maintain a high level of quality that satisfies everyone. We talk about the importance of good communication. We discuss the balance between processes and people. While sophisticated processes may be necessary, relying too much on documentation can lead to missed opportunities or overlooking potential solutions.

Listen to this episode now while we dive deep into these points below:

What knowledge gaps exist regarding the implementation of these new analytical techniques, and how can statisticians and data scientists bridge them to maximize effectiveness of clinical research?

Despite the promises of Bayesian approaches, the lack of expert training, software tools, and computational resources has made their adoption slow. Statisticians and data scientists should invest in training programs to enhance their skills in Bayesian modeling, use open-source software for Bayesian modeling, and ensure that they have access to well trained computational analysts. 

It is also essential that statisticians and data scientists effectively communicate the results of these analyses to clinicians, regulatory authorities, and maybe even patient groups to encourage adoption.

Bayesian approaches have great potential to improve the accuracy and efficiency of data analysis in early-phase clinical trials. But Bayesian methods are not without their challenges, but with adequate training, resources, and communication, they provide opportunities for novel ways to cope with complex problems in clinical research.

But, first comes the challenge: prior elicitation from clinical information. Developing prior distributions can be difficult without the right set of tools and resources.

In this episode, Miguel Pereira, a statistical consultant for a German-based company COGITARS specializing in early clinical trial design, and I highlighted ways to tackle these challenges.

We also discuss the following points:

For years, creating data visualizations has frustrated statisticians, data analysts and data scientists.

Software - especially SAS - hasn't been great and organisation have tried to take the pain out of the process by creating standard figures.  

But these standard graphics fail to communicate the message to the intended audience in the best way.

Great data visualisations consider the needs of an audience in terms of language, understanding, and context, as well as the communication channel and the message to be conveyed. With so many elements to take into account, the design space for visualisations can be overwhelming with factors such as color, filtering data, animation, and uncertainties all coming into play.

Unfortunately, many senior people in large statistics organizations focus only on regulatory requirements and don't invest time in understanding the importance of visualizations.

In this episode, I discuss why it is difficult to create a great standard data visualization and what organizations can do to improve their data visualization game.

Have you been considering a one-armed observational study? 

As a statistician, it is important to understand the various types of studies that are used in data gathering. In this episode, we will be discussing one-armed observational studies and why they are, in most cases, not a good idea. We will also touch on the early days of my career as a statistician and how I initially approached one-armed observational studies.

We also discuss the reputation problems of these studies and the scientific problems such as: