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- The A-B-Cs of creating your Data Science team
- How Data Science fits into an Org
- Recruiting and Hiring the Right Data Science Team
By VICTOR ANJOS
Recruiting for data science teams
On to the finer detail of how to actually get hold of some of these data science people. It’s worth starting with a reality check from Neil Yager, Chief Scientist at Phrasee. He says, “In general, this is a challenging task and people should manage their expectations up front. This is a relatively new field and demand is high. Therefore, the pool of available talent is rather limited.”
It’s for this reason that the number of vendors offering embedded cognitive computing functionality has skyrocketed over the last couple of years. There are now hundreds that offer some machine learning capability, with martech a particular growth area.
The shortage of expertise means that if you are going ahead with your own in-house team, your first hire and the team leader is particularly important. Yager explains: “..due to high demand and short supply, salaries tend to be at the high end. My recommendation is that the first hire be someone relatively senior and experienced. Don’t be tempted to build a larger team of less experienced people — this will be counter productive in the long run.”
Neil goes on to recommend companies “attend or host local meetup events for big data, data science, AI or machine learning. These are active communities, and the people who attend these events tend to be very engaged.”
However, even if you dive into your local Hadoop meetup, you may not find the person you need straight away. Data science teams often employ people from a variety of analytical or scientific backgrounds, precisely because it’s hard to find somebody with all the skills you need.
Maloy Manna writing on the Data Science Central blog says:
“There are actually probably just a handful of the “unicorn” data scientists on the planet, who have superpowers in maths/stats, AI/machine learning, a variety of programming languages, an even wider variety of tools and techniques, and of course are great in understanding business problems and articulating complex models and maths in business-speak.”
Of course, maintaining links with academia is also important (these will probably cross over with meetup groups). Most companies using data science (including the previously mentioned ASOS and Channel 4) will work with PhD students and a university, as well as employing graduates into their first jobs.
Finally, if you want to read how a tech unicorn goes through the recruitment process, Riley Newman, head of analytics at Airbnb, has discussed how they interview their data science candidates over on Quora.
Data science team roles
Here are a selection of roles that may be needed in your data science team. Ultimately, some of these roles may overlap, and you may not need one of each – it depends on what your team wants to achieve.
Team leader
The team leader must have chops when it comes to data science. Leadership and business skills alone are not enough. Christopher Doyle, who works in pricing and analysis at Aspen Dental, sums this up well:
“A new analytics team absolutely needs a leader who possesses strong mathematical modeling skills. The reason is simple: Mathematical modeling skills are hard to learn and require years of experience working under experts. While data mining and business savvy skills are certainly valuable, these should ultimately be secondary considerations, since they are skills that can be easily learned.”
Data strategist
We have discussed this role already as the bridging point between the data science team and the business. This role may work with campaign experts from the marketing team.
Data strategists may be similar to product managers, and may need to work with front-end developers and UX professionals as part of a wider data product team.
There may also be data analysts involved, much like on a more descriptive analytics team, who do data processing and may also visualise data.
Data scientist
As the Venn diagram of data science suggests, a data scientist should have expertise in both statistics and software development. They will likely be able to use Hadoop or Spark to analyse large datasets and they will be familiar with R or Python. The team leader will be a data scientist.
What kind of data scientist should you hire?
Data engineers and architects
These roles are about understanding how data is structured in the organisation. That means databases, cloud computing, distributed frameworks like Hadoop and some programming languages expertise.
Data architects capture, organise and centralise data. Engineers then test, maintain and get the data ready for analysis.
Elizabeth Mazenko has done some great research for BetterBuys on what capabilities various members of the data science team typically have, and provides a chart which makes a useful rough guide.
You’ll see many more job titles mentioned in other articles – data hygienists or business solutions architects, for example – but most should correlate with the three or four roles outlined here.
A final thought for marketers
Christopher Doyle, director of market analysis at Aspen Dental, writes:
“Even though the marketing department is our top customer, I prefer keeping them at arm’s length. Everything in the marketing department needs to happen immediately, so keeping some distance between them and the analytics team allows the analysts to manage the workflow more efficiently.”
Though marketers and Agile digital teams may have just got a taste for iterating and innovating, data science can take time. From data cleansing (which could take months) to developing models and implementing products, marketers need to understand the scale of investment (both time and money) required in data science teams.
However, once these teams start to bear fruit, advantage over the competition can be significant.
A final, final thought
Parry Malm, co-founder of Phrasee:
“Here’s another option: in 1997, I sat next to a guy in a university computer science class who was called Neil, who’s now known as Dr Neil Yager, our chief scientist. So another option is to build a time machine, go back 20 years, and make sure you’re sat next to the right person.”
Whilst data science has many grey edges, it’s probably worth including some fairly dry definitions of two common teams – ‘Big data analytics’ teams and ‘data product’ teams. The former looks for predictive patterns in data without necessarily having a preconceived notion of what they are looking for, and the latter works to implement automated systems that are data-driven.
So what do you do when all signs point to having to go to University to gain any sort of advantage? Unfortunately it’s the current state of affairs that most employers will not hire you unless you have a degree for even junior or starting jobs. Once you have that degree, coming to my Mentor Program, with 1000ml with our Patent Pending training system, the only such system in the world; is the only way to gain the practical knowledge and experience that will jump start your career.
Check out our next dates below for our upcoming seminars, labs and programs, we’d love to have you there.