According to Glass Door, the average salary for a data scientist is $118,709. It is even higher in San Francisco: $126,875. So this is not a decision to be made lightly.
To make sure your company is ready for this significant addition to the team, ask the following questions:
Have you assessed your current resources?
A data scientist cannot work in a vacuum. Before you consider hiring one, first assess your current resources. Make a list of all existing hardware, software, people, available skill sets, and processes. It is often possible to use familiar products as Hadoop tools.
Knowing what you have on hand first can help you decide just how much more you need to add to the equation before you have a functioning big data environment. Otherwise, bringing in a data scientist can lead to unexpected up front costs before you can even put him to work.
Are you ready for a paradigm shift?
The entire point of hiring a data scientist is to fine tune your business by delving into details not readily apparent without doing some digging. If you and your company are not ready for a paradigm shift, you are not ready to bring big data on board and hire a data scientist.
Surveys show that introducing big data elements to the equation is a big shift. They use words like “game changing” to describe how big of a shift it creates. If you are only looking for small changes, you are not ready for big data.
Is everyone on board? If not, can they be gotten on board?
Consider this scenario: You work for an insurance company. Insurance is an old and conservative industry. It is also a very data intense industry. It is seeing a lot of changes due to new technology and the use of big data tools. For example, they have created real-time data analysis tools which allow customers to get a car insurance comparison in real-time with their competitors.
This is a team effort. Everyone from customer facing call center representatives to the CEO needs to understand how a change like that impacts the company, how it is implemented and how to engage with the new process.
If customer facing employees do not understand it, they will give bad information to the customers. If managers do not understand it, they will not train their people properly. Everyone needs to be on board for this to be game changing in a positive way. Otherwise, it can be game ending.
If there isn’t across the board buy in already, it can help to introduce changes incrementally, then measure the results and educate everyone about the value of a data driven business environment. Seeing is believing. Instead of arguing with people, give them a small taste of success and let them see for themselves how much of a difference it makes.
Can you insert a data scientist effectively?
In order to be effective, a data scientist needs more than just access to data. They also need to be able to work effectively with other people. They need feedback from other employees so that they know what matters to the company and they need to be trusted and taken seriously so that the conclusions they draw do not get relegated to the dust bin while people carry on with business as usual.
If you think you are going to stick this guy in a room full of computers and spreadsheets and be done with it, you are not ready to hire a data scientist. You need to figure out he will fit into the hierarchy and connect effectively with other people. Hadoop security and data governance concerns should be taken into account and made streamlined as possible for the entire team.
Does your company have a learning environment?
Bringing a data scientist on board is like adding a headmaster to your learning environment. If the company does not have such an environment to begin with, there is nothing to plug him into.
A company that is not already asking questions, solving problems and learning on the fly is not ready to be taken to the next level by someone who does that for a living. If you do not have a creative culture that fosters thinking and questioning, you need to get that first before you hire a data scientist.
Currently, big data is a big deal for business. It can leverage information and other resources. Many companies feel they need to keep up to survive. But in order to keep up, the company needs to be ready for this change. Do not hire a data scientist until you have laid the groundwork to make it work well.