I hired my first data scientist - when can I expect to see results?
A hiring manager asked this question, and the answer is not so simple. But great question!
A hiring manager approached me recently and asked for advice on building data science capacity. He proudly shared that he hired their first data scientist for their department, and inquired when he can expect to see results from this hire. This is a great question!
He didn’t beat around the bush- he wanted to know his ROI (return on investment) on this rather expensive resource- good on him for asking the right questions! Unfortunately, the bottom-up approach with building data science capacity often fails, as the successful endeavours to build data science capacity usually begins top down, or started bottom-up and some insightful executive picks up that bottom-up approach and makes it their own, with executive sponsorship, business case, authority to operate, budget, resources, project charter, etc- the whole nine yards!
But here we are, with this hiring manager, his bottom-up approach in an AI-naïve organization, and his simple question. He hired his first data scientist- when can he expect results? Let’s focus on his question then! Any experienced data scientist worth their salt can look for the low-hanging fruit and come up with some quick results right away. This new data scientist should be able to align with you and your domain experts and come up with targeted solutions to some of your specific use cases and particular problems in the department the manager helps to direct and manage.
However, if you can’t align your data scientist within your organization’s larger data science community of practice (DS CoP), then what you will get is just another point solution that does not align with the Enterprise. Data science requires a community of practice, otherwise, you only get your own employees doing the same things, according to their own local standard. If your data scientist stays in your silo, and the other data scientists in other siloes in your organization do the same, then your organization will not be able to form the Enterprise standard of practice that makes sense for your organization’s mission statement, and not just for your department’s siloed vision.
To make matters worse, if your organization does not align with other external organizations and their DS CoPs, then your organization risks not aligning with industry standards of practice in this ever dynamic and maturing enterprise data science CoP.
So how do you form a DS CoP? If it’s bottom-up, then this will require you, the hiring manager, and your newly hired senior data scientist to start networking with all your data scientists and their management in your organization. Commit to meeting regularly and sharing problems, pain points, and how to support one another. A good way to keep this informal network going is to have some meetings where you present your work to one another. Soon, you will find your DS group meetings growing organically, especially when other data scientists and managers provide feedback and support on how to make your solutions better. Soon, you might also form AI peer review groups on the AI projects each department has to build. This is how a grassroots, informal DS CoP starts, from the bottom-up.
But this is just the beginning. To cement this approach, you must make these activities visible to your directors, and eventually to the executives. Soon, with perseverance, struggle, and some good old fashioned luck, this DS CoP will be the driving force in your organization for AI initiatives, and who knows, you might even get executive support, business case, authority to operate, budget, resources, project charter, etc- the whole nine yards!
If you're looking for support, here is how to contact me:
Coaching and Mentorship: I offer coaching and mentorship; book a coaching session here