There is a strong and immediate demand for Data Science in all industries, but a limited supply of qualified data scientists. There is also a fuzzy understanding of how data science actually works. Data science is "black magic" to most managers. These factors result in companies paying a high premium for data scientists today.
In such a competitive job market, how do you build your own AI / ML / data science team?
Data Scientists: to PhD or not to PhD?
Data scientists are often thought of as PhDs in math, statistics, physics, industrial engineering (specializing in optimization problems), or computer science (specializing in data mining). But does a data scientist really require a PhD? A leading AI consulting firm revealed to me what profile they truly look for:
The ideal data scientist has:
Soft Skills Are Incredibly Important
In a competitive talent market, it can be tempting to hire only for the hard skills above, but soft skills can be really important such as:
The Data Scientist is a Scientist!
Remember that this is a scientist, not an engineer. Meaning they are strong in the scientific method: start with a hypothesis, test, observe and disseminate results, and recognize that often the experiment will fail. And that's a good thing! Failure is learning. This is different from an engineer whose focus is to build and make something work.
Data Scientist: The Role
In practice, a data scientist works with business stakeholders to understand and identify business problems and goals. Their goal is to uncover business value, which could include:
Often it starts with a business question no one knows how to answer. But before trying to figure out how to solve it, the data scientist must assess: is this even the right question to be asking? What data do we have to answer the question? What's the geometry of the data? Where can the data be sourced from? What's the best way to attack the problem?
Once the problem, the goal and the approach are established, data scientists run experiments. They will generate hypotheses and then run experiments on small and large amounts of data to confirm or disprove each hypothesis, progressively learning more and refining potential solutions.
A data scientist's deliverables are usually business presentations with visualizations and recommendations, and ultimately, an algorithm, to be implemented in practice by a team of engineers.
Industry Examples of Data Science Problems
What Does a Data Science Team Look Like?
A project can be experimental research, or it can be to roll out a commercial product. Either way it includes work such as feature engineering, modelling and training, ETL (collecting, preparing and cleaning data), algorithm development and execution, API development, application development, measurement, product roadmapping and project delivery. Although some data scientists do everything end-to-end, the more popular pattern is to have the data scientist work in a multi-disciplinary team with:
The "Chief Data Scientist" Persona
The Chief Data Scientist comes in many forms with different titles such as Director Analytics, Principal Scientist, Lead Data Scientist, SVP Data Science. This person is typically a data scientist themselves but also has a responsibility for working with executive stakeholders to get buy-in and funding, aligning the team's projects to business objectives, leading and mentoring the functional team, and reporting overall results.
Since this is not a business person by training but they must play a business role, this will often be a data scientist with leadership intuition and business savvy. This person must excel at communicating with non-scientist executives, justifying investment, explaining the results of experiments (even failures are a good thing!).
Data Science Talent Wars
Today the demand for data scientists exceeds the available supply of talent. At one California conference I attended, the Chief Scientists of big name brands like LinkedIn, Mashable, and Uber took advantage of their speaking spots to actively announce their recruiting needs to the audience, and were seen actively networking the room!
A common way to recruit talent is looking at PhD candidates in academia. Big companies like LinkedIn take time to publish their work and ensure they have a big presence at academic conferences so that they get in the face of potential candidates.
When discussing offers, salary is a base factor. In California, a data scientist could expect anywhere from $90,000 base salary (junior) or as much as $250,000 base salary for a data scientist leading a team of 5-10 people. But more than simply compensation, data scientists, like most employees, are looking for:
And as in any noisy job market, watch out for "fake" candidates who have simply stuffed their resumes with buzzwords!
Outsourcing Data Science?
As with any valuable skill, managers will have a preference for hiring full-time staff if they can. But in recent years, the alternative of using data science consulting firms and AI/MI agencies has risen as an alternative.
For managers who are exploring the outsourcing option, here are some considerations:
Potential opportunities to outsource:
What are the hot AI skills?
Data scientists get hit as much as 100 times a day with solicitations by recruiters. They complain that recruiters are terrible. They don't understand the domain or the practice, the challenges of the role, the job. Investing in a recruiter with real understanding of data science can make all the difference, as well as getting data science leadership directly involved in meeting candidates, just like the Chief Data Scientists of LinkedIn and Uber who were networking the room at the local conference I went to. The candidates will instantly recognize and appreciate it.