Opinion: How to hire great data talent in 2021
There’s a lot of hype around AI, and for many years now, businesses have been navigating the journey to assembling large data teams in hopes that positive results will follow. But staffing for data and AI initiatives is no easy task: data teams are complex, nuanced organisations with different kinds of people using different tools, and data talent is more in-demand than ever.
Hiring managers are often faced with several staffing challenges when starting out or scaling the enterprise AI journey, starting from the most fundamental one, which is a lack of specificity around the business’ AI needs. Companies that have not gone through the essential planning or assessment of what their data and AI needs are can end up passing on incomplete (or unnecessary) requirements to hiring managers, which can lead to poor hiring choices and high turnover.
Beyond this, the hiring and assessing of qualified data talent can also be problematic. Existing staff need to be armed with effective interview techniques to properly evaluate whether a candidate brings the right skills to a team. And when a team is up and running, it’s up to managers to ensure that formal upskilling, which is fundamentally critical to AI and staffing, is in place and running efficiently.
The good news is that with a comprehensive approach to understanding different data profiles and skill sets — mapping them to the organisational needs at every stage of the AI lifecycle — plus the right combination of targeted hiring and upskilling, companies can start building for a sustainable enterprise AI initiative.
Who (and what) should AI hiring managers be looking for?
Many companies stumble at one of the first challenges in their AI hiring journeys: knowing who (and what) to look for when building successful data teams. Surprisingly, much of this is down to the fact that AI hype often focuses only on the data scientist role, expecting it to cover every skill and specialism across the organisation related to data and its operations.
The first rule of thumb is to ditch the idea of hiring a data unicorn — an all-in-one data wizard who possesses every skill required to conceptualise, create, maintain and productionalise successful data models that can drive business decisions. These people may exist, but they are extraordinarily expensive to hire, and they may not be what organisations need anyway.
What businesses really need is both a diverse set of data related roles and profiles. There are many skillsets beyond the realm of the data scientist that can support and work with each step of the AI lifecycle, and it’s just about finding out what skills the business needs and mapping that to the roles required. These may be data professionals who focus on stats and algorithms, or those who are specific vertical experts. They may also be data analysts that have some coding experience in SQL, SAS or another language, and who have a good general understanding of databases and infrastructure.
In order to go beyond the AI hype and successfully implement a comprehensive and sustainable Enterprise AI strategy, you need to be able to dissect each part of the data journey, translate it into concrete organisational resources and needs, and then map those needs to the different data roles and profiles available. You might just find that where you thought you needed a unicorn, a data analyst, data manager or machine learning architect will do. Beyond that, you may also find that you’ve already got the resources in house.
Key questions to ask surrounding data profiles
Now that the concept of life beyond the data scientist is firmly in mind, it’s time to consider what the needs of the business are and which types of data profiles will add the most value. Considering this well before the job posting stage is essential, as it will help hiring managers to identify and list specific skills, which will influence the questions asked in the interviewing stage.
Here are a few ideas surrounding key questions in understanding an organisation’s immediate needs for data projects:
- Can you define the data projects the organisation will tackle?
- What are the final, expected outputs of these projects? Will they be:
- Smaller scale (for example, dashboards or analytics for internal use, or more geared toward a self-service
- Operationalised models in production impacting a large part (or parts) of the business?
- Is data for the projects readily available, or will part of the projects themselves be around finding and mining new data sources?
Data-driven initiatives fuelled by machine learning, and AI platforms are a clear win for data teams and, when implemented the right way, can provide insight to businesses that can drive pivotal business decisions. However, the challenge of handling future growth must be balanced with the reality of hiring and upskilling team members with diverse profiles and skill sets that are appropriate for your business model.
Ultimately, finding a good mix of data professionals that is the right balance for the business is key to staff retention. A well-oiled machine means happier employees, with fewer people having to perform tasks outside of their skill or comfort zones.