Think your business needs a data scientist? You are probably wrong.
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When I started my career in data 15 years ago, I could never have imagined a sexy rebranding of my work with the coining of the term “data scientist”, not to mention the immense popularity it he has acquired in recent years. Widely considered one of the hottest and most sought after jobs in the world, data scientists are rewriting what it means to be cool in the age of modern technology. There’s never been a better time for my fellow nerds. Jobs are overflowing with demand far exceeding supply. The industry has gotten so hot that it’s not uncommon for startup board members to demand the hiring of data scientists early in the product lifecycle. It is in this capacity that I frequently meet with leaders and most often inform them that they do not need a data scientist.
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How can a data evangelist like me say that this sudden interest in all things data science is about to backfire? Before I begin, let me start by saying that there are indeed many good reasons to hire a data scientist! I’m not going to argue that data science isn’t necessary or useful, because when used correctly, it’s an incredibly powerful business weapon (yes, I’ve been there with “weapon”) ). I’ll just say it’s an overused term with little formal accreditation that refers to a wide range of data-related activities, not an ordered suite of skills that can be learned in a 12-month course. So when it comes time to hire, organizations need to think hard about when and what type of data scientist your organization needs.
When potential new clients come to me, at least 50% of the time, it’s under the guise of “My CEO/board member/etc told me I need to hire a data scientist.” To which I generally ask the following four questions:
1. How much data do you have?
I say four questions, but many organizations never get past the first. If you’re a startup and haven’t launched yet, you don’t need a full-time data scientist. Full stop. In fact, even if you are well established but with a small customer/product/member base, again you don’t need a data scientist. Why do you ask? Because unsurprisingly, data scientists need data. Not just any data. Many techniques require a minimum of tens of thousands or even hundreds of thousands or even millions of data points to construct.
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Currently there is a huge focus on deep learning. Job descriptions for data scientists are awash with terms like neural networks, machine vision, and natural language processing (NLP). The problem? These types of techniques rely on massive amounts of training data. Consider the widely popular Google Translate, a type of neural network built on a lexicon of over 150 million words. The amount of data needed to successfully deploy these types of models is beyond what many companies have.
There are many techniques that use less data than deep learning, however, they still require reasonably large samples, not to mention a working knowledge of when to use which methodology. There is still valuable work to be done at this point to create an environment where data science can thrive in the future, it just doesn’t require an expensive full-time resource.
2. Have you established key performance indicators (KPIs) and regular business intelligence reports?
Without a basic understanding of what drives the organization, it will be very difficult to use advanced techniques. For example, a data scientist can use machine learning to make predictions such as which users will unsubscribe or become very active, however, if the company has no definition of unsubscribe or very active, it becomes a requirement before creating the predictive models. . Also, it’s hard to validate models if you don’t have enough metrics to evaluate them. Other techniques such as A/B testing require an advanced selection of an overall evaluation criterion (OEC), which is usually a business-focused KPI.
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3. What do you think this data scientist will do once hired?
Perhaps the most subjective and interesting of the questions I ask, “What do you want this data scientist to do?” The most common answer I get is. “We don’t know, that’s why we have to hire one.” In this case, I kindly tell the organization that they are setting up their data scientist to fail. You don’t have to be a data scientist to hire one, but you should have a good idea of what’s possible and what’s not so you don’t set expectations. unrealistic.
Data science isn’t magic, and it’s not even mainstream science. It’s as much an art as it is a science, which means the variability in skills and abilities is considerable. You may even have existing team members who can grow into many data science applications. An easy entry into data science for an existing analyst is to start forecasting the KPIs they are already reporting on. Here, they have the opportunity to learn about data that is familiar to them, which is not only good for employee morale; investing in your people now means less need to recruit in a highly competitive market in the future.
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4. What support networks are available to your data scientist(s)?
If you don’t have the right support network for your data scientists, don’t bother investing in hiring them. In recent years, there has been a huge increase in data science programs, but most graduates are simply not ready to tackle business problems without a careful grip. The vast majority of programs ask students to solve pre-established problems on their own data. In the real world, you want your data scientist to help you figure out which problems are fixed, and clean data never exists.
Hiring a junior data scientist without a senior resource for guidance can not only lead to frustration on the junior’s part, but also often lead to poor analysis. Junior team members tend to have trouble translating business issues into technical ones and a poor translation can lead to months of work on a product that misses the mark.
This problem is not completely alleviated by hiring more seniors, in part because it is extremely difficult to certify that your senior recruits are actually good and competent. If you’re lucky and hire a talented and driven data scientist, she’ll still need a lot of executive-level support to be successful. Imagine a situation where templates are created but never used because there is no buy-in from team leaders. Or when A/B tests are performed but the results are ignored. Worse still, the tracking data needed to analyze a problem is not collected at all.
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Often a necessary first step is a robust data collection program, which is likely funded by a database engineer or administrator, not a data scientist. In many organizations, the senior data scientist(s) spend an exorbitant amount of their time simply fighting over data needs and the deployment of their team’s work. It’s a surefire way to lose that talented and driven data scientist.
The landscape of hiring and retaining good data science talent is competitive and expensive, but being smart and conscientious about when, who, and how to hire can mitigate the pain and costs. Don’t fall into the trap of job postings that are endless lists of skills. Don’t expect magical pixie dust from your data scientist. Take stock of your real needs and, if possible, consult a trusted professional before hiring. The success of your data program depends on it.