Secrets to Attracting Top Analytics Talent in a Hot Market

. October 5, 2018 . 0 Comments
Rhea Trupe, a Sr. Global recruiting Lead for global executive recruitment firm JBCconnect, is one of the few executive recruiters specializing exclusively in data science and analytics roles, for marketing and other organizations.

In the era of data-driven everything, the need for marketing and business analytics professionals has never been greater. Annual demand for data scientists, developers and engineers is growing fast. By 2020, there will be nearly 700,000 openings, according to IBM. At the same time, the number of jobs for all US data professional roles will increase by 364,000 openings to 2,720,000.

Simply put: There just aren’t enough trained professionals with technical expertise to keep up with this level of demand. What’s more, the field is changing rapidly. Companies are more demanding, programs less structured and roles more complex. Crunch the numbers and what does this mean? Competition for analytics talent is exceptionally fierce.

So how can companies navigate this landscape as they develop in-house analytics functions? Be knowledgeable and realistic about the market, clearly define your program’s goals, and be smart your recruiting process.

How the Talent Market is Evolving

Hiring managers should be cognizant of two major trends: the increase in specialization and the emergence of hybrid roles.

The number of specialized analytics roles, from data analyst to chief data officer, is growing. It helps to understand what these roles are and how each might fit within your analytics program. However, if you’re not fully immersed in the field, it can be difficult to keep track of new job descriptions.

One issue is that titles aren’t consistent, as the underlying position descriptions tend to vary widely. For example, a data analyst at one firm may need a completely different set of skills from a data analyst working at another company or in a separate industry. Without accurate information, organizations will find it near impossible to define – and fill – the precise roles they need.

This is complicated by another trend: To get more out of existing resources, organizations are creating hybrid positions at every level. For instance, they may want a data science engineer who specializes in marketing and request a candidate with experience as a data analyst, data scientist and data engineer. The position description would include a combination of some or all of the following requirements:

  • Strong predictive and statistical modeling experience focused on marketing data
  • Expertise in SQL, SAS, R, Python or other programming language to query data and perform analysis
  • Experience building data infrastructure, data pipelines, ETL processes and data warehouses
  • Understanding of marketing mix modeling, multi-touch attribution or cross-channel attribution to measure marketing activities
  • The ability to leverage data to gain insight into trending, user experience and behavior to drive digital marketing, mobile and social strategies
  • Expertise in visualization and reporting tools like Tableau, Qlikview, Looker etc.
  • Artificial intelligence and machine learning expertise for predictive learning and analytics assessments
  • The knowledge to drive selection and automation of KPIs, reporting and analytics to tell the story behind the data
  • Strong consultative skills to present these findings to key decision makers.

Candidates Hold the Cards

Yet the biggest challenge for hiring managers is that candidates have the upper hand.

Today’s analytics pros aren’t looking for a job because they need one. Their skills are sought after in all industries, and they know it. Thus, they can be picky with their next career move. In fact, most analytics and data professionals aren’t driven by salary or title.

What does matter to them?

  • The opportunity to work with cutting-edge technologies
  • The ability to make an impact
  • Meaningful work they love
  • A company that excites them

Companies can’t afford to overlook these desires, which are major contributors to luring the best talent

However, even companies that do check all of these boxes may still lose top candidates. The reason? They take too long to hire. Many extend the interview process for weeks before making an offer. This sluggish pace simply doesn’t cut it in today’s competitive market. With two to four offers on the table at any given time, candidates aren’t in the mood to wait. Speed is crucial, even more so if there’s a unicorn in your pipeline.

How to Approach Your Talent Search

The best way to build your analytics team is to define the goals of your analytics program. All too often, companies will hire analytics talent without clear direction. This is usually a mistake. You simply can’t design and decorate the interior of a home until the foundation and walls are built. As strategy evolves, so do job responsibilities. If employees find themselves in jobs they weren’t hired to do, they’ll inevitably become dissatisfied and quit.

But when companies put strategy first, outcomes improve.

For example, organizations frequently make the mistake of hiring junior analysts before investing in leadership, believing they can get the same results at a lesser cost. But junior-level professionals don’t have deep enough technical backgrounds to handle the workload.

One way to avoid this situation is to be transparent about the maturity of your analytics program. Some candidates coming out of large corporations may be nervous about moving to a smaller team without much structure. Being upfront with a candidate will help you weed out those who are reluctant to build an analytics program from scratch.

The most critical role to fill first are analytics leaders. They’re instrumental in developing the data strategy and building the program’s foundation. Once this is in place, organizations have a clear idea of the specialized talent they need to hire.

Data engineer and architect hires typically come next. That’s because data scientists and analysts can’t do their job if there is no proper data infrastructure in place. Only after the organization builds a foundation can it start hiring professionals with specialized skill sets.

It’s also a good idea to work with a knowledgeable recruiter who has experience helping clients put together top-notch analytics teams. They can advise hiring managers on current market trends, competition, salary expectations and much more. Not only will this save time, but it also ensures your organization hires the right talent to meet your goals.

Faced with a fiercely competitive market for talent, organizations will need to be smarter and more strategic if they want to find, attract and hire the right people for their analytics teams.


Wanted: Emerging Data Analytics Roles

  • Data analyst
  • Data scientist
  • Data engineer
  • Data science engineer
  • Data architect
  • Business intelligence analyst
  • Big data engineer
  • Data modeler
  • Analytics strategy manager
  • Market research analyst
  • Chief data officer
  • Chief analytics officer

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Category: Articles, Basics, Culture, How-To, Organizational, People, Strategy

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