Happy autumn! Summer flew by, and with it so did my intentions to follow up to Part 1 of this post. In that post I talked about how to start learning data skills on your own and applying them to side projects. I also pointed out some great existing resources on applying for jobs. Now several months later, here are my thoughts on considering job offers. I say this as somebody who still feels fairly new to the analytics field, so please chime in if you want to add something to round out my thoughts!
Consider Your Industry
In Part 1, I pointed out that all the nuts and bolts of resume writing and cover letters are already on the internet and I don’t need to repeat them here. But one thing that is worth mentioning is to consider the industry where you will apply to jobs. A lot of data jobs are in the tech industry, but they are not the only ones who need your skills. I think that one of the reasons I was successful in pivoting to a data-centered role was that I stayed in the transportation industry where I had been working for awhile and had education. Knowledge of the ins and outs of transportation planning, land use, and travel behavior gave me a leg up over somebody with stronger data skills who had only worked for Amazon.
Leverage the experience you have in a specific industry. They need data skills in healthcare, education, and finance too. Use the people you already know to find those you don’t who are doing work in that field that you might be interested in. The job might not feel as flashy as a big tech firm, but you will be able to contribute faster and have more time to spend growing your skills when you’re already familiar with the lingo, concepts, and people in your line of work.
Considering a Job
While advice on looking for a job abounds, there is a lot less advice out there on what to do once you get an offer or are getting into the final round of interviews. Here are some questions I recommend asking yourself or the hiring manager:
What’s the role? Try to understand in as much detail as possible what you’d be doing. This will set realistic expectations. There is a vast array of jobs with “data scientist” or “data analyst” in the title and it can mean very different things. You don’t want to get caught thinking you’re going to build models and realize they just want someone to do data visualizations.
Who’s on the team? Think about the team you’d be on and who you would be able to look to for guidance, both peers and supervisors. It is essential early on to have people smarter than you doing the same kind of work and for those people to be available to answer your questions. When I left my last job, which I really enjoyed, for my current job, it was largely for the chance to work on an analytics team that I could learn hard skills from.
Why you? At the same time, you want to understand what niche you’d be filling on the team. Try to understand if you have specific skills that the team is really hoping you’ll use – are those skills you want to use or things you’re trying to get away from? If you are developing a skill, is there someone with a PhD in that field who would get all the work instead of you, or would there be opportunities to try out your new skill?
Starting a Job
Become an expert: Identify something that most others at your organization don’t know how to do. Get good at it. Then teach others how to do it by leading trainings at work if there is an avenue for that. In my current and last jobs I have led courses in survey design, and not only has it helped me meet people, but it’s helped establish me as someone who is skilled in that topic. Being known as skilled in this area has helped me get placed on projects and established my team as an internal resource, which builds collaboration and keeps us busy. It’s also been great practice for my communication and speaking skills.
Get involved: If you have the energy outside of work, join professional organizations for analytics but also for the industry that you will be working in. Meet people and learn as much from them as you can. I joined Women in Data and found it is totally worth the $120/year. You get access to online courses at datacamp.com (which almost pays for itself), mentorship, career coaching, and a local chapter with events. I am also involved in cross-disciplinary organizations in the transportation industry such as WTS International.
Go Deeper
At some point it may be time to go deeper in your skill development, perhaps once you’ve been working for a bit. I found I really enjoyed the math side of my work (as opposed to straight programming), so last year I did a nine-month online Certificate in Statistical Analysis with R through the University of Washington because I wanted more structure, interaction, and credibility than a free MOOC course could give me, and I did not have a degree in a quantitative field. It was time-consuming but very worthwhile for me. I was also privileged in that my employer at the time paid for it. Regardless of your situation, this is definitely an investment in time if not money, and you should have a clear sense of what you will learn and what it will do for you before such a large commitment.
Nerd on the Bus 