How Long Should You Stay at Your Analytics Job?
Given the flurry of media attention surrounding Big Data, data science, and the Internet of Things, it’s no surprise that the hiring market is hotter than it’s ever been before, and with the plethora of opportunities available, one of the most common questions I hear from candidates is, “I don’t want to be a job hopper, but how long should I stay at one job?”
In the current economy, it’s becoming well-established that the likelihood of a professional staying with the same company for 30, 20, or even just 10 years is decreasing, especially early on in their career. As our culture becomes more on-demand, favoring contractors and temporary assignments, the stigma around job hoppers is disappearing. In fact, in my experience, I’ve found that HR is more likely to ask, “Why did she stay at that job for so long?” than, “Why did she leave so soon?”General guidelinesAs a general rule of thumb, staying at a job for less than a year will likely raise questions during the interviewing process. For quantitative professionals with less than 15 years experience, 2-4 years at each job is ideal, but 5 or more is getting too long. With 15+ years’ experience, the average tenure tends to stretch out a bit.
2017 Update: In our latest research, we found that the average tenure for predictive analytics professionals who changed jobs in 2016 was 2.0 years, a drop from 2.6 years in 2015, and that over 22% of analytics professionals changed jobs in 2016.
I always encourage those early in their career to broaden their horizons. While you’re unencumbered by a house that you’d have to sell, or not worried about transplanting a family, you should take advantage of it. It will enable wider industry and domain knowledge, which will lead to greater opportunities as you climb. If you invest too much in one company or industry early on it could kill your long-term career potential.How do you know if it’s time to move on?Aside from the general guidelines above, what are some other signs that it might be time to seek new opportunities? First and foremost – if you’re not learning, it’s time to make a change. If you’re repeating the same tasks over and over and making no progress it will not benefit your career. Some repetition is to be expected, as your employer is hoping for some return on their investment of hiring and training you, but you should strive to be continually learning and keeping your skills fresh. For quantitative professionals especially, it is crucial that you adapt to changes in technology and tools.Just as it is important to evaluate your personal career growth, it is also important to be constantly evaluating your current company and industry. If a company is stagnant, isn’t adapting, or isn’t embracing data-driven decision making, now more than ever this is a red flag. Slow-growth industries can be good for building experience, but be careful not to stay too long - unless the company is doing something unique it is a career graveyard. It may be time to evaluate your options if your contribution is not valued, if there are rumors of consolidation, poor performance, or activist investors advocating for “cost cutting” (a.k.a. massive layoffs).What about loyalty?Now I know that some of you may be asking, “What about loyalty?” While loyalty is something to be applauded, you must ensure it is well-placed. This is why it is especially critical to continuously assess the industry you’re in and the company that you’re working for, and not just when you first evaluated the job offer years ago.Especially in large organizations and in the current bottom-line oriented business environment, longer tenure can begin to work against you. It also can be easy to mistake complacency for loyalty and let your skills atrophy, which is very dangerous in an ever-changing discipline such as analytics. While broadening your skill set or scope of responsibility could be one important reason to change jobs, making a prudent change can also be an opportunity for a title or salary bump.The more proactive you are about managing your career, the easier the transitions will be, and the more opportunities you’ll be able to take advantage of. Don’t wait until you’re looking for a job to reacquaint yourself with the skills that employers are looking for. If you’re already searching, check out my lists of the biggest career strategy mistakes and the biggest job search mistakes that I see Quants making. Best of luck!
Interested in our salary research on data scientists and predictive analytics professionals? Download our studies using the button below.
Click to download our free salary reports