Six Secrets You Must Know Before Hiring Data Professionals
Introduction
A lot more than just a CV and a few interviews goes into hiring considerations, well at least for employers looking for top talent. A resume is a representation of one's skillset and can't communicate an individual's personality, life paradigm or full technical ability.
As a former data analyst who has transitioned into the recruitment agency space, I understand firsthand the intricacies of finding the right talent for data-related roles. Having been in the industry myself and having built teams tackling challenging data problems, I know that making the right hiring decision is crucial. But what exactly should you look for? Based on years of experience and lessons learned, here are six secrets you should be aware of before making your next data hire.
1. Technical Skills are Just the Start, Not the End
You might be tempted to place a lot of emphasis on technical skills, given the nature of data roles. While it's essential that candidates have a strong foundation in mathematics, analytical thinking, programming and domain-specific tools - don't make this your sole criteria. Data roles, contrary to popular belief, aren't all about coding or crunching numbers (trust me I thought this when I was breaking into the industry). They’re also about understanding business problems, making strategic recommendations, driving impact and communicating with stakeholders.
So look for well-rounded candidates who can demonstrate strong technical skills but can also think critically and understand the bigger picture. This can be assessed with a combination of technical, case study and soft interviews.
Check out DR Analytics Recruitment's technical interview resources here.
2. Language Agnostic but Willing to Code
Programming languages come and go, but:
Willingness to learn remains invaluable.
Foundations of programming will always be important.
So instead of focusing solely on whether the candidate knows Python, R, or SQL, pay attention to their approach to problem-solving and their willingness to adapt and learn new languages or technologies. A good data professional should be able to quickly pick up new languages as required and apply their skills flexibly across various problems.
I mention foundations as knowing the principles around syntax, data types, variables, control structures, functions and exception handling are often taught by institutions and if not know should be trained in-house as a priority.
3. A Strong Grasp of Probability and Statistics
In data science and analytics, nothing is ever cut and dried. Professionals need to have a probabilistic view of the world, understanding that data insights are often shaded with uncertainty. Note, the level of knowledge will change of different roles: Anyone you hire in the data science space should be comfortable with the concepts of statistical significance, Bayesian thinking, and probabilistic models. Whilst when it comes to business intelligence, more intermediate knowledge will suffice. This is not just about crunching numbers but about making informed decisions even when the data are noisy or incomplete.
4. Attention to Detail and Root-Cause Analysis
Data roles often involve sifting through large sets of data to find trends, anomalies or insights. This requires an impeccable attention to detail and an unyielding curiosity to understand the root cause behind the data. Whether it’s cleaning messy data or digging deeper to understand an outlier, the best data professionals are those who can scrutinise the data at a granular level to arrive at actionable insights.
But how do you find this out? Most clients we work with either have their own case study testing or we encourage them to implement them. Conducting these live helps to assess the attention to detail.
5. Don't Underestimate the Importance of Soft Skills
Soft skills like communication, teamwork, and adaptability are often as critical as technical expertise. Data professionals need to collaborate with various departments, explain complex data findings in layman's terms and adapt to ever-changing business environments.
Here, I've found that the ability to convey complex concepts or processes to non-technical stakeholders is key. This is because the value of data & analytic becomes elevated when more stakeholders understand what is actually going on behind the scene or dashboard.
6. Inclusion of Team Members in the Interview Process
Your current team knows what skills, qualities and types of personalities will complement their own strengths and weaknesses. Involving them in the interview process can provide valuable perspectives that you might have missed. It also helps in assessing cultural fit, ensuring that the new hire will be a good addition to the team not just in terms of skills but also in terms of teamwork and collaboration.
Conclusion
Hiring the right data professional is a complex but crucial task that requires a multifaceted approach. It's not just about what’s on the resume, but also about soft skills, cultural fit and the ability to adapt and grow. Making a mistake in hiring can be costly in terms of both time and resources, so taking a comprehensive view of candidates is essential for long-term success.
Remember, we’re not just hiring for a role; we're hiring for potential, adaptability, and the overall betterment of the team and organisation. Keep these secrets in mind, and you’ll be well-equipped to make the right hiring decisions in the data space.
