 Hi, everyone. It's Monica Wahee here with some data science career advice for you. I initially wrote a blog post where I explained how it is that I can do something for you for $4,000 that a typical public health statistics and research consulting company will charge you $10,000 to $20,000 for. And that is write your peer-reviewed paper. Basically, I explained how these consulting teams are put together and how they work together to develop the paper. My initial point was these teams are highly educated and experienced and therefore expensive. But then I realized that a lot of my LinkedIn learning learners and other customers of mine out there are probably trying to apply for these jobs at some of the public health consulting companies. So I made a video explaining more about these teams, how they are put together, how they interface with the customer and how they work together to get the job done of writing your peer-reviewed article. In that video, I just focused on three typical positions. Lead epidemiologist or researcher, project coordinator and statistician or like data scientist. I briefly described each position and I talked about how the positions work together. But in this video, I'll explain more about each position and I'll also talk about how these teams work together with each other and how they interface with the customer. So in the last video, I started by pointing out that no matter who writes your peer-reviewed paper, certain tasks need to get done no matter what. Then I explained how public health research consulting companies batch these tasks up into roughly three positions on a research team. Okay, here we go. Here's our example team. We have a lead researcher who will do scientific oversight, a project coordinator who will do a lot of paperwork, writing and organizing, and a statistician or data scientist who will do the data management and statistics on the project. Let's start by talking about how this team is put together. These teams may either work on site or off site from the customer, but now in our pandemic era, probably everyone is working at home. Even so, there is an organizational chart and a chain of command. Both the project coordinator and the statistician typically report to the lead researcher on the project. But as I mentioned in my previous video, the lead researcher generally is expensive and therefore has fewer hours on the project. The data scientist and the project coordinator have most of the hours, so they end up having to work independently and collaboratively a lot without the guidance or help of the lead researcher. And they all still work for the consulting company, so the lead researcher reports to someone higher up at that company. The company often does not place a very heavy hand on the researcher. In fact, researchers tend to lose their jobs at these companies unless contracts come in that need their specific expertise. Consequently, these lead researchers tend to get really friendly with the customers. They get good at cultivating customers and finding a way to make more work in contracts for the company over time. So you can see why a research team might coalesce around a lead researcher who is really good at getting contracts and work for her team. But then you also have the customer organization exerting pressure on the lead researcher. All the communication and direction should go through the lead researcher. But you can see from the diagram how competing agendas between the consulting company, which is trying to make money, and the customer organization, which wants the research done, might cause the research team to feel like a pinball in a pinball machine if the lead researcher is not a good leader and manager. Which brings me to my next point, which is with the setup, what could possibly go wrong? Unfortunately, the answer is a lot. But it doesn't have to. If you arm yourself with the necessary skills for each position. So, let's say you want to be the lead researcher. I'll be honest, it's hard to manage a statistician and a project coordinator. These are two totally different jobs that attract totally different personalities and you have to manage them from afar with not many hours dedicated to the project. That's why most of the work falls to the project coordinator and as a lead researcher, you always feel like you are totally dumping on her. Most scientists don't really have much in the way of leadership or management skills because that's not what we train for. But a lead researcher position at a consulting company really demands top levels of these skills. And since these positions generally don't get much respect from the scientific world, these jobs unfortunately attract academic failures. Scientists who couldn't play well with others in academia. But there's a way to take advantage of the situation, especially if you just graduated with your doctorate. So you don't have much experience, but you know you do not want to go into academia, at least not right away. Here's my top career tip for you. Go out there and gain some exemplary higher level management skills in one of these lead researcher positions because then you will forever be ahead of most of the scientific world which lacks these skills. So here are my tips for succeeding as a lead researcher on one of these teams. First, do everything you can to learn better management skills. Knowledge you typically don't get in college. And you can start by taking my online course, Microsoft Excel for Managing Research. This course starts by showing you the proper way to store data in Excel in Chapter 2. Then we go over using Excel in Research Management in Chapter 3. I show you how to track grants and conferences in Excel in Chapter 4. And finally in Chapter 5, I show you my tips for using Excel to manage research teams. Hey, just as my thank you for watching this video, I'll give you a coupon code for 50% off that Excel course. Just enter the code Leader21 at checkout and it will apply a 50% discount to your registration for the course. Everyone tells me it's worth it because they learn a lot. Okay, so what else do you need to know to succeed as a lead researcher? Not only do you need management skills, but you need a super process that you use for each project. The process needs to be logical, transparent, and involve the team. And the team has to buy into it. What my recommendation to you if you want a good process as a lead researcher is to take my LinkedIn learning courses in designing big data health care studies. This two-course series walks you through a big picture process of designing a study aim or hypothesis, planning your analysis, and then ensuring that the analytic data set you develop and the statistics you do answer the study aim or hypothesis. Once you get good at the process, you share it with your team. Then, as you go through roughly the same process with every project, your team kind of expects it and feels comfortable with what you are doing. I also strongly advise you to get a mentor and set up a formal mentoring relationship. In fact, you can ask about this when you get hired. You can say you really want to be hooked up with a more senior researcher at your consulting firm who will meet with you once a week for an hour or two and help you go over your work and mentor you. But unfortunately, don't be surprised if that doesn't work. It's not so much that people won't mentor you. It's mostly that people are horrible at mentoring, I've found. Some mentors are even dysfunctional and try to compete with you. So if you want me as a mentor, yes, little me, feel free to inquire about what I jokingly call my rent a colleague service. I actually have mentoring skills because I've been doing it so long. And when you think about it, meeting with me twice a month for some mentoring and encouragement is actually pretty cheap. So consider contacting me about it. I'll cut you a deal. But ultimately, the most important thing is that you ensure that you know where this position fits in your long term career goals and what you want to accomplish when serving in this role at the consulting organization. Don't take your eyes off of the prize. So now we make it to the project coordinator. If you watch my other video, you know, I think this is a very challenging and often thankless job. You are expected to know how to do everything and to actually do all kinds of things, but you are rarely given much credit. And your team members are often a source of stress. If the lead researcher is a great manager, then you'll have a great experience and learn a lot from them. But if they are toxic, like they are a failed academic, looking for a cushy job in the wrong place, you could really have a miserable time. And statisticians and data scientists are especially hard to work with, even ones who are very good at their jobs, because their jobs are so isolated from the rest of the team. The project coordinator often has a lot of trouble managing them because of this. And the statistician is very narrowly skilled. So even though their contribution is worth a lot, it doesn't amount to much in the end. So much more work needs to be done by the project coordinator. So my professional tip for people who want to try out the project coordinator position, learn to type very fast and take notes really well. In fact, you probably want to take my data curation course. Data curation skills are skills for documenting data and things to do with data. For example, if you have a survey, you'll want a way of curating how the survey questions connect with the variables and the data that are the answers. I show you how to make all those helpful files in this online course on LinkedIn Learning. Okay, so what's my advice to you so you can succeed as a project coordinator on one of these consulting teams? I always say, she who writes the minutes writes the meeting, which is my way of saying whoever writes anything down basically gets to say what we are all doing and what we all agreed to do in the meeting. If you decide to be a project coordinator, be that person, the person taking notes and writing everything down. Then step two, organize all these files and make it so everyone can get to them, like organize them on a central server and use naming conventions and versioning for all the files, literally develop policy and enforce it. So you tell me, Monica, I actually don't know how to do that. And I say, what do you mean? You don't have a system of naming conventions for like everything, code files, variables, datasets, just sitting around for you to use? Well, here then, use mine. I set up a system for you that I write about in my book, mastering SAS programming for data warehousing. Don't worry, you don't need to know anything about SAS to be able to use my naming conventions. They are covered in chapter four, managing ETL and SAS. I explained how to designate storage and user groups and set a policy around that. And then I described setting naming convention policy. I give you a naming convention system for datasets, variables, code files, and metadata files that help you version files together as a group and also not lose anything. Basically, to succeed, you gain management skills, like in data curation, and those skills from the Excel course. And you essentially try to manage the team from the bottom. Finally, our last position on the team, which is statistician or data scientist. Now this person is either alone or on a team of statisticians. These are the people in charge of the data, no one else, not even the lead researcher. So this creates issues because it's easy to have a communication breakdown between the statistician and the rest of the team. Also, because a statistician is isolated in the role, they can turn into a bottleneck easy. If everyone is sitting and waiting a week for an analysis, all they do is blame the statistician. Then the statistician has to wait a week for the team to read and understand what she develops, but somehow no one blames the team for taking a week to work on it. Also, if you are alone on a team and you run into a serious data issue, you can really feel alone. The lead researcher and project coordinator often cannot help you. And you have to tap into your personal professional network, or post on Stack Overflow or something. It can be a high pressure environment when that starts happening. My professional tip for the data scientist is similar to the advice to the project coordinator, and that is to be hyper organized. Take my data curation course on LinkedIn Learning I mentioned earlier, and work with the project coordinator to ensure proper data curation exists on the project. Read that chapter in my SAS book I mentioned earlier, and make sure you are keeping your code organized and using naming conventions. Basically, insist on being managed by the project coordinator and the lead researcher and make it easy for them to do it. So here's my advice for succeeding in a statistician or data scientist position on such a team. Okay, you can tell I am not kidding about learning data curation, because that's my main advice to you. Do your data curation. Really, the project coordinator and lead researcher should all be chipping in and working on this, but in reality, no one I have ever met knows how to do it. That's why I made the online course in data curation foundations. So go take the course and then do the work that the other team members won't do. Better yet, tell the entire team to take my course, gain the skills, and help you with the work. Many hands make late work. I guarantee that is a true statement. And my next tip is, no surprise either, set up your naming conventions and storage policies and be absolutely religious about following them. And share them with others on the team and try to get the whole team on the same page with naming conventions and file organization. If you take my LinkedIn learning courses and analyzing data in SAS or R, you will find that I actually teach some of these managerial skills along the way. In addition to learning the software language, you will pick up a lot of good habits from my courses. So what's the best scenario where everyone is cross trained and everyone has overlapping skills? It's when the project coordinator seeks to add data skills to her portfolio, where the lead researcher seeks to add management and other professional skills to her portfolio. And when the data scientist seeks to add management and scientific skills to her portfolio. Really, this is the best attitude and approach you can take in the field of research and data science. Always think of cross training and gaining new skills. And please, let me help you. If you are willing to sign up for my one to one mentoring program, I will meet with you once per week or every two weeks, whatever is best for you on Skype or Zoom. We will work for one to two hours and I'll help you plan your work and work your plan. I'll help you figure out the best way to do all the work you are getting assigned at work. That way, I can teach you skills based on real work. So they are practical and you can quickly demonstrate your success. I made this video to help you figure out where you might fit in a public health research consulting firm. If instead you are someone who works at an organization that wants to hire one of these teams, then please watch my third video in the series where I provide practical advice to individuals developing contracts with such teams. I hope this video helped pull back the curtain and give you a sneak peek inside the world of public health research consulting. Thanks for watching.