Welcome to Expert Viewpoints: A Closer Look at Job Listings. In this video, experts will share how to recognize a good job listing and features that should make job seekers cautious. I write the job postings for my team, and I can tell you that generally speaking I try to avoid making a laundry list of skills that I want from a candidate because that will only scare away most candidates. So, you'll end up with a very restrictive pool of overconfident candidates and it's not a great way of you know selecting the best person for the job. Instead, I think that in general you're better off trying to capture the spirit of the kind of candidate you want, the kind of developer that you want. Make a very short list of skills that they really need to have as opposed to a major wish list. I also want to make sure that in the job posting that I’m not just saying what I want from the candidate but also tell them what we offer. So, what can we give them, what kind of experience we give them, how will they grow as developers. So usually, the descriptions are a little broader, so people tend to put in a list of technologies they require people to have skills on, but in my opinion, you can basically learn anything. If you have a good foundation. For example, if you know, I don't know, in Apache Spark SQL you can easily learn, HBase or Hive or, or Impala or something. Same, if you have experience in TensorFlow, it's usually not a big deal to learn PyTorch then. For an entry level data scientist, usually you need to have at least a strong background in data science, data analysis and, data visualization, but for people who are, and then some amount of coding in Python and SQL will be helpful. But for senior and principal data scientists, understanding the business problem, having a, learn to create a roadmap of solving the problem and identify and say, what are some of the important ways we can solve the problem, figuring out what kind of a methodology should be used. I would say that depending on the role I think we should see the responses for this particular question would be different. But I would say overall, I think I would say that the position description should match the resume, the LinkedIn, and the position description, all be lined up in respect of the position, what you're applying for. A very good job description, shares expectations in the role, such as what you'll learn, what upward mobility looks like. You might also see salary of course, with the way that the market is moving. It also has who you'll be reporting to. There are some features and job descriptions that I definitely steer away from. If it has very little training, they want you to hit the ground running. We're a fast-paced team. Sometimes that could scare someone away. If there's no salary listed and, or a big list of requirements that cover multiple roles, I've seen data engineering, typical requirements listed in a data scientist job description. I've seen machine learning engineer requirements in a data scientist job description. If you see a big, huge list of requirements and you realize that this is what a data engineer does, or a business analysts or data visualization engineer, that might be a red flag that they want you to be a full stack or someone that does everything. And the organization hasn't really refined and defined what they want this person to do.