I'm an experienced (but bored) analyst that wants to move into a data science role and be trained/mentored by experienced individuals. But when I look for positions all I see are experienced data scientist or data analyst positions. Is there no in-between? How does one transition?
If you search on Indeed.com for 'junior data analyst' or 'junior data scientist', there are quite a few positions.
Another thing to keep in mind is that postings often slightly exaggerate the amount of experience needed, esp. measured directly in years. A good way to make yourself more marketable and get off the 'I don't have a lot of experience'-hump is to do a few data science side projects. Common projects involve writing software to analyze the firehose twitter stream, participating in Kaggle.com competitions, doing a cool data visualization to tell a story in a unique way, etc. I recommend doing a bit of everything to demonstrate your interests -- data scientists have to be able to tell stories, communicate with non-technical people, often ship code to production depending on the company, etc (varies from company / company).
Use datasciencemasters.org to fill in any gaps in your knowledge, do lots of side projects, start going to data-focused meetups (meetup.com_ near you, and start reaching out to data scientists over LinkedIn and get to know the space more as well.
The way we've laid it out, there are some core skills we want you to have. The most succinct way to describe the requirements are we need you to be fluent in data. Able to explore and transform it, communicate your discoveries in non-technical ways, and able to discuss it with other analysts in a very technical manner.
Beyond that we understand the perfect data scientist is really a team of data scientists who complement each other's skill sets. Because of that we have a bonus skills section. We don't expect the right candidate to be great at all of them but there should be some unique strength that they bring to the table.
EDIT: Oh and P.S. side projects are fine and dandy but they don't speak to us very well. The problems tend to be handed to you on a silver platter. I want to see how you can go from discovering business needs to choosing the right data to analyze with the right techniques.
I've been taking coursera classes and the like but it helps to work on 'real' projects with folks to see how it's really done outside the classroom. I really should push myself to get out into the DS groups, but I find them intimidating when I have no practical experience.
I'm what I would consider to be in a junior data science role. (Less than a year out of undergrad, no advanced degrees yet.)
From my experience, start actively looking for and doing more data science type work in your current position. I wasn't brought on as a data scientist, but once the majority of my work was in some way related to data science, my company was more than happy to change my title when I asked.
You'll have to actively learn while working, but I think it's completely worth it. I've definitely learned more because of the pressure of work than I would have otherwise doing side-projects.
TLDR: Functionally become a data scientist and the title will come.
I've been trying this strategy in my current position for a while now but they don't value the in-depth analysis I'd like to do which is why I'm looking elsewhere. I'm glad to know that it is successful in other organizations!
