5 Things to Review Before Accepting That Data Scientist Job Offer
Before you get too excited and sign the papers for that new data scientist job, and solidify your role as a new hire, make sure you look over these 5 things first.
If you're reading this, you've probably received at least one data science job offer. Congratulations! That's an accomplishment in itself, especially considering the competitiveness within the data science job market.
But, before you get too excited and sign the papers that solidify your role as a new hire, there are things to look over first. Here are five of them:
1. The Salary
According to PayScale, the average salary for a data scientist is $92,949. It drops to $88,973 for an entry-level position. Hopefully, you've already researched the wages for data scientists with experience comparable to yours before starting the job search. If not, now is the time to get those numbers and see how they stack up to your job offer.
Consider things like the cost of living, too, especially if the job requires you to move somewhere new. Moreover, evaluate whether you will have to endure a significant commute to get to work each day. Are public transit routes available? Answering those questions could help you determine whether the salary offered will help you have a comfortable lifestyle.
When you're in the negotiating stage, the company may ask, "What is this job worth to you?" Even if it comes across as a casual question, it could be an attempt to see whether you'd accept a low figure.
2. The Other Perks
The salary is undoubtedly critical, but you should also take a big-picture approach when looking at what a data science company offers its employees. Label Insight is a company that uses data science to bring more transparency to food labels. It also provides excellent benefits for team members, including unlimited vacation days, a dog-friendly workplace and a stocked kitchen, plus more conventional advantages like subsidized health insurance.
Think about the things that would make a data science company an ideal place for you to work, and assess how the enterprise that offered the job matches those preferences. Maybe you'd love the ability to work from home one day a week or would appreciate if the company frequently pays for its workers to attend data science conferences and classes.
If a company doesn't offer most or at least several of the benefits you want, that's not necessarily a reason to turn down the offer, but it could give you matters to negotiate. For example, the business may not have a remote working policy in place for all its employees, but perhaps your role is more suited than most for working independently.
3. Whether You're an At-Will Employee
Pennsylvania is an at-will state, along with Virginia, Texas and 11 other states. What that means if you live in one of those states is if a contract identifies as an at-will employee, your employer can fire you for any reason — or no reason. Look for a clause that states your data science contract may end at any time, and think carefully about accepting the job if you see one.
Some companies decide they need to invest in data science mainly to keep up with competitors. But, they often don't have concrete plans for how they'll use data science or how data could help them solve specific problems.
In a case like that, imagine the distress you'll experience if you're under the impression the contract will last for at least three years, but an at-will clause allows the employer to terminate you, and they do because their data science expectations haven't come to pass. That likely happened because of inadequate resources, not a failure on your part. But, an at-will clause could still cause you to lose your job.
4. The Job Responsibilities
What the job entails has likely already come up in many of your previous discussions. But, you should still look closely at the job offer and ensure everything is as you expected. While looking at the job specifics, you might notice some red flags. For example, maybe the contract says you'll be the sole person involved in data science at the company.
Or, perhaps the human resources representative mentioned the company wants to start an advanced analytics program, but the contract says you're the team leader for that endeavor and will only direct two other people. In that case, the task may be too big to tackle, and you should look elsewhere for a job.
An ideal job should challenge you, but not make you feel as if you're continually trying to reach impossible expectations. Even if you graduated at the top of your data science class or have several years of experience under your belt, you won't be able to thrive if the resources available don't align with the duties the job requires.
5. The Working Hours
The job contract should also state how many hours per week this position involves. Moreover, examine the contract to see what happens if you work overtime hours.
In the United States, federal law dictates that you are an employee eligible to receive overtime, you'll receive your base rate of pay plus 50% — often called time-and-a-half. However, not all workers can get overtime pay. Working as a data science freelancer or earning more than a certain amount per hour could mean overtime pay is not an option for you.
Your data science role may require working outside your usual hours from time to time, such as to finish up a project that has a tight deadline. But, if you don't get overtime for doing it, think about the possibility that you may end up resenting what seemed like a dream job because you work too hard for the pay.
Being Cautious Gets Results
It's tempting to accept a job offer almost as soon as you get it. But, the best approach to take is to ask for time to look it over and review the factors above and others that matter to you. Showing caution like that helps you determine if the job is a good fit for you.
Bio: Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal and VentureBeat, and has been writing for more than five years. To read more posts from Kayla, subscribe to her blog Productivity Bytes.
Original. Reposted with permission.
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