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This is a contributed article by Scott Zoldi, Chief Analytics Officer, FICO.

Try googling rock star and data scientist, and prepare to be amused. Its actually a thingusing rock star and data scientist in the same sentence. Dont get me wrong, I get it. As a data scientist myself, working with some of the most brilliant minds in the industry, Im amazed by the creativity, intelligence, vision and raw talent of my colleagues. They collaborate every day, harmonising their strengths and expertise around responsible AI to solve the big issues facing our business, our industry and our world. Theyre working to correct financial inequity and disparity. Theyre developing machine learning to stop financial crime and money laundering. Theyre developing tools and platforms for others to leverage at scale. Their set list is long, and Im proud to be their biggest fan and collaborator.

Executives sometimes say to me, You make AI sound easy. How can my company get started? First, its not easy, often complicated by the team structure and organisational philosophies at play. Second, you start by building a rock star analytics team a carefully selected ensemble that balances each data scientists strengths, while also recognising and addressing capability gaps on the overall team.

Its an up-front investment that wont come cheap. Demand for data science talent is high. However, demand for AI products is also up since the onset of COVID-19, according to a recent Corinium survey. If youre thinking of building your own group of analytic artists, here are a few guidelines to consider.

Before assembling a team that makes beautiful music together, you first need to stop, take a hard look at your organisation and ask questions. What are you trying to accomplish with this team? What resources do you already have in place technology, expertise and executive sponsorship to support this team? What are your companys data analytic strengths and weaknesses, and how can this team impact those areas? How will this team engage and communicate with others within the organisation and deliver value to the business? Will this team engage externally, with customers and industry peers? What is your budget? How will you measure the ROI of the team?

Theres no template or magic formula for getting it right. In fact, 65 percent of AI leaders admit that building a team with the right skills is a significant barrier to AI adoption, according to a July 2020 Corinium report. Furthermore, its worth exploring how to incorporate greater gender and ethnic diversity as you set out to build your analytics dream team. According to a McKinsey report, companies and teams with greater gender, ethnic and cultural diversity outperform industry peers by up 33 percent.

Its an iterative process where you ask the hard questions early and often to produce a successful outcome. First and foremost, the team should appropriately balance the companys current level of analytics sophistication and aspirations for AI adoption. From there, you can determine the right size and capabilities of the team based on organisation-specific needs and objectives.

Once you set the stage, then you can focus on talent. The key here is diversity look for a mix of skills sets and talents. Think of it this way: you only need one Elvis. In turn, he needs a band of great musicians to be successful. Indulge me as I run with this analogy and share my thoughts on key positions that comprise a rock star analytics team.

In my (admittedly biased) opinion, todays data scientists have earned their rock star status. Theyre transforming our world with AI-driven processes that fuel next-level performance and better business outcomes. But, before jumping on the bandwagon, take the time to consider whats right for your organisation. To build a balanced, functional team that fits the needs of your organisation, be selective when choosing your team and take the time to understand the unique role each scientist plays in the band.

Scott Zoldiis Chief Analytics Officer atFICO,driving the company's innovation in artificial intelligence and incorporating it into FICO solutions.While at FICO,Zoldihas been responsible for authoring 110 analytic patents with 56 patents granted and 54 in process. He is an industry leader in developing practical applications and standards for AI, Explainable AI, Ethical AI and Responsible AI, and was named one of Corinium's 2020GlobalTop 100 Innovators in Data & Analytics.

See the rest here:

Calling all rock stars: hire the right data scientist talent for your business - IDG Connect