Tell me if this sounds familiar. Your job description includes data science and data visualization. You’re the go-to expert for building the architecture and the point person when something goes haywire. You’re expected to make sense of the data lake and ensure it’s labeled correctly. Plus, they need you to have a sixth sense for what the business needs and how to measure those ROIs efficiently. Your company tried to hire a unicorn and instead they got you. Before that falls on you like a cartoon anvil, consider this. They had faith in your abilities. You stood out as someone who could help turn their data situation around, and that’s a good thing. It’s not your fault that your job description is a ten-in-one deal, but now it’s time to fix it. Your boss needs to expand the data science team yesterday, and here’s how to make sure that happens.
Frame the Situation
There’s a really good chance your boss doesn’t understand just how much is on your plate. Data Science is a hot field, but the umbrella term isn’t doing it any favors. If your boss doesn’t have hands-on experience with data science, he or she may not know just how much goes into what you’re trying to do.
Before you storm into the office with an idea, do some homework first. What’s your boss’s background? What field is your company in? Do you know anything about the boardroom’s experience? Framing a situation is all about minimizing risk, and you’ll need some background information to make this work.
Most of your higher-ups may not be aware of how many different jobs you’re currently doing. You’ll have to help them understand that not expanding is riskier than letting you continue to do the job of ten. You could illustrate with an example from your company’s field.
“Would you ask an electrical engineer to design the bones of a new building? No, you’d get a commercial architect for that. Despite their related projects, you need both inputs.”
From there, lay out how management can quickly assess the success of this new project. Specific goals, instead of one long term vision, could help make the process easier to swallow. For you, that means assessing the one thing you could hand off that would make an immediate difference in your day to day operations.
Consider Your Company’s Needs
The structure of your team needs to be realistic for your company. In an ideal situation, you’d have every position covered, including complicated IT personnel. In reality, you’ve got budget constraints, board members to convince, and existing projects or hiring that take priority.
The best option is to consider the structure of what your company needs. For example, if you’ve got quite a bit of in-house IT structure already, you may want to go for an IT-centric plan that leverages existing infrastructure and trains in-house tech personnel to move into the role of data analysts.
Your company might benefit from the opposite end of the spectrum with a dedicated data science department. If you don’t already have the in house structure in place and your company is making heavy use of big data, it could be time to hire someone to build it, someone to manage it, and someone to train it.
Companies that fall somewhere in between could take a more integrated approach with a SaaS infrastructure option and a few key people on the team. Whatever option you think is best, clearly outline your recommendation, highlighting the other suggestions and why they won’t work.
Go in with Plan A; Follow up with Plan B
Your Plan A is an ideal situation. With an open-minded boss and a great pitch, you may get your Plan A with few revisions. However, if Plan A is rejected, you can still salvage the situation with a well-timed Plan B.
Your Plan B could be one aspect of your Plan A that’s a necessary component. For example, if you want a data science team, but that’s out of the question, go back to that one pain point you have. The one position you need to be filled to make your job more efficient and more consistent could be the sell you need. You’re spending all your time labeling data, and that’s why your projects are late. A part-time data analyst could allow projects to move down the pipeline three days faster.
Expanding the Data Science Team
The ROI for a well-hired data science team could give your company a considerable lead over the competition, and it’s your job to help your boss see the potential. No one wants to spend money in a vacuum, but plenty of people will spend money when the possibility of a much bigger return is at stake. Outlining the ROI, framing the situation to minimize risk, and preparing a logical Plan B could help get your data science team on track. Follow up with some tried and true people skills and you could get straight to that yes.
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