Hiring a Marketing Data Scientist

Qualified marketing data scientists are a rare breed.

Qualifications to seek on your quest to find the best.

By Guest Blogger Katie Tingley, President and Founder, The Tingley Advantage Inc.

Imagine your business gaining market share and outpacing your competitors’ growth rate because you found a way to identify meaningful, actionable insights that engage your customers like never before. Marketing data scientists are the heart of a company’s ability to evaluate what is happening in a business and to model solutions that will take advantage of a new opportunity or mitigate a challenging situation. But hiring the best marketing data scientist can often be a daunting challenge.

A Rare Breed: Strategic Marketing Data Scientists

Over the past few decades, data science has come into its own as a business competency and is now one of the hottest careers in marketing. However, having the technical capability to interpret data, create visualizations or even generate a predictive model are no longer the only things companies look for when hiring a marketing data scientist.

The holy grail of data scientist qualifications is someone with the ability to understand both the numbers and the context of the business situation. Finding professionals with this combination of technical expertise and business acumen — strategic data scientists — is an elusive goal, but it is possible.

Strategic data scientists are a rare breed, a veritable “unicorn” in the world of data analysts, and the quest to find them is a big topic among CMOs and other marketing leaders. To help with this quest, we’ve identified several strategies to pinpoint sources for candidates and asked top marketers for insights into their own strategies for filling these gaps.

What makes a good strategic marketing data scientist?

There are several characteristics that candidates need to possess or have the ability to develop. Some are specific to the field of marketing data analytics, while others involve more generalized business acuity.

These include:

  • Solid business training, practical experience and, ideally, industry experience that enables them to ask the right questions before delving into the data.
  • Familiarity with best practices for analytics projects and processes, such as CRISP (the Cross Industry Standard Process for data mining), allowing them to use a structured approach when tackling a given situation.
  • Strong communication, visualization and storytelling skills so they can present data to various users in a way that reveals insights and leads the audience to an ultimate conclusion.
  • The ability to spot trends both in the industry and in the data so they can anticipate market needs and capitalize on them before competitors.
  • A general curiosity; the desire to read between the lines, or between the numbers, as it were.

Identifying Candidate Sources

After defining the skills and other qualifications needed, there are three main avenues for sourcing strategic marketing data scientists:

  1. Seek out and hire pre-established expertise, adding it to your team.
  2. Build your own expert, by taking someone with the right potential and training them to meet your specific needs.
  3. Partner with external experts who can hit the ground running, without taking them on as permanent team members — in a word, outsource.

Hire Expertise

The goal here is to find a candidate with the data analytics expertise you need and bring them into your organization. Identifying candidate sources can involve a number of different tactics, but the best results often come from ongoing relationships that have been cultivated over time.

For example, I am a member of a Program Advisory Committee for a local college where I interact with college staff and instructors who have insight into the top students and their strengths. When the need arises for a resource, I have an established pipeline on which to draw.

Other potential sources for candidates include local undergraduate and graduate programs, and college internships or co-op placements. Nurturing relationships with these organizations can provide a huge pay-off.

If you’re seeking an experienced professional, local chapters of statistical societies or data science meetups will draw potential candidates who are committed to keeping up with industry trends. These events can be a treasure trove of potential resources.

Likewise, your established network of colleagues may be able to guide you to experienced individuals with proven track records in the field. Employee referral programs can produce highly skilled candidates, since high-performing employees often have friends with the same skills and attitudes toward their work. As a bonus, the recruitment cycle can often be shortened when using a referral program, as the candidates have been somewhat pre-vetted by your existing employee.

Build an Expert

Russ Benblatt, head of marketing & e-commerce (VP) – North America at Crabtree & Evelyn, offers this advice for sourcing strategic data scientists: “The best luck I’ve had is attempting to build them myself. Take a great strategist and make the offer to train them to be a data scientist. Not cheap, but it gets you what you need.”

Erin Hoskins, vice president – e-commerce at Allied Health Media, agrees: “… I generally start with someone who is a great marketer and who loves data, then train them for the more technical aspects.”

A person with strong strategic skills and an aptitude to learn statistical and analytical processes will often be well worth the time invested. In the retail space, programs such as the National Retail Federation’s RISE Up program may provide a pool of high performers who are eager to learn and grow; these can often be the best candidates.

When taking the “build from within” approach, establishing a hands-on mentorship program is highly recommended. This will help create the infrastructure needed to build skills necessary for your business. Defining strategic level KPIs and rewarding those behaviours can motivate employees to excel in the areas you need them to, whether they be employees with the potential to become new strategic data scientists, or those with the ability to train and mentor them.

Outsource Your Data Scientist Tasks

If you don’t need an in-house, full-time resource, and/or if there isn’t time for resources to get up to speed, outsourcing to a qualified marketing data scientist may be your best bet. When outsourcing, determine if you have a short-term need that will occur once every few years, such as developing a segmentation or a predictive model, or if there is an ongoing need for a more in-depth level of expertise than you have in-house. Many outsourced agencies can provide help on either an ongoing basis or a project basis. Understanding the level of support you require will help you evaluate the best partners, as well as whether outsourcing will be your best bet.

Regardless of how you fill the role, it’s important to set expectations for your data scientist candidates. Matthew Polk of Verbena Consulting recommends promoting the role in terms of “… what you want the analyst to do in the bigger picture (in addition to the analysis tasks and systems), and this should frame expectations of interest for bigger picture, more strategic thinkers even in the youngest of talent.”

Narrowing the Field

Qualified marketing data scientists are a rare breed.Once your data scientist candidates have been identified, potential employees should be vetted against the set expectations for the role. As with typical recruitment, experience, skills and fit with the team are always important. Additionally, it is necessary to assess a candidate’s technical capabilities and their business understanding.

As Polk offers, you want to assess candidates “… for talent with quant[itative] skills and [that are] inclined to ask and want to understand ‘why.’ Some people are attracted to the doing of data analysis while others are attracted to what can be done for the business (or their internal customer/client) with the insights they can provide.”

As you are vetting candidates, balance analytic and technical questions with business questions, so you gain a holistic understanding of each candidate’s ability to assess a situation and apply the correct data technique. Ask for examples of work product, or school projects if that is appropriate, to really see their skills and abilities first-hand.

Real-World Testing for Data Scientist Candidates

Once a candidate has passed the first few interview hurdles, you’ll want to gain an understanding of how they would tackle a real-world situation. A proven approach is to create several mock assignments, congruent with your business needs, to evaluate each candidate’s skills across the specific dimensions you are seeking.

Ensure that the business problems you present them with are realistic, and let them know their response should include an explanation of their approach, why they chose said approach, their work and the result. This can be an onsite assignment or they can take it offsite, with a defined turnaround time, often 24 hours.

You may wish to include some or all of the following elements into this assignment:

  • A business problem and a set of related data, with the question of how they would approach the problem given the information they have.
  • Different scenarios, with the question of what analytic method they would recommend for each.
  • A business problem, with the instruction to break it into two sections of response: a situation analysis, followed by a data analysis and visualization exercise.

You may wish to have your prospective marketing data scientists create a written response and/or present their work to a small group. This enables you to evaluate not just the quality of the work itself, but also the candidate’s ability to communicate their work effectively to a target audience.

A Necessary Investment

Finding strong resources is never easy. With a specialized skillset such as strategic data science, the challenge is even greater — but it is critically important. By investing time upfront to define the needs of your business, identify appropriate sources and thoroughly evaluate candidates’ abilities to deliver those skills, you can set your business up to take advantage of the opportunities that solid data analytics provides. It’s an investment, but one that businesses can no longer afford not to make. As Arthur C. Nielsen, founder of the A.C. Nielsen Company, once said: “The price of light is less than the cost of darkness.”


CCG’s retail experts can act as your ongoing or interim marketing data scientists — or help you identify the right on-site person for your needs. Learn more about us and our retail capabilities and services.
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Katie Tingley is the Founder of The Tingley Advantage Inc., a performance improvement company that partners with corporations to bring focus to their strategic initiatives in the areas of performance measurement, marketing analytics, and process improvement. She provides advisory services on how to best approach an initiative and then works with the in-house team to ensure the project is delivered with the highest quality. She’s partnered with companies such as Interpublic Group (IPG), Johnson & Johnson, Imperial Parking Corporation, Sirius XM and Sears.

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