How to avoid the most common stumbling blocks in the hiring process
Written with Jeremy Hill
Tech recruiter with over a decades worth of experience

A recruiter is an information broker. They connect companies looking to fill positions with people that can fill them. The best recruiters in the business take the time to get to know both Hiring managers and prospective hires and network to bring them together. Companies rely on the hiring manager to bring in talent and build functioning teams. It’s a delicate balance of needs and expectations for multiple parties. These are the three most common stumbling blocks encountered during this process.
1. Looking for the perfect match

The reality of what is available might not be a person who is in the same industry, has the same background as the position you’re looking to fill, and a long and storied job history. More importantly, the perfect match might not be what ends up working the best anyways.
1A. Failing to recognize the importance of soft skills
For very technical roles, it’s an easy trap to select a candidate who has almost every hard skill that you’re looking for.
But sometimes soft skills are just as important in whether a person is successful in that role. In particular,
- Project management abilities
- Storytelling skills
- Creativity in problem solving
One client hired an exact match to their desired profile. This person checked every technical box: they had worked for a large competitor, had the technical background and the experience. The problem? After 8 months, he hadn’t closed on a single sale.
It turns out that the big name of the old company was all it took for him to close on his previous sales, and when he moved to the newer smaller company, he no longer had that advantage. The smaller company really needed someone who knew how to disrupt the industry and find creative ways to bring technology to market.
Then they hired someone with a mechanical engineering background and a history of creativity, but they had to learn everything about robotics, the subsurface industry and subsurface engineering. She ended up being very successful, brought in tens of millions of dollars in sales and stayed at that company for five years.
Rule of thumb
You should interview the person if:
- They have100% of the hard skills but only 50% of the soft skills
- They have 100% of the soft skills but only 50% of the hard skills
If you’re a little more open-minded and adjust your expectations, you might be surprised at the jewels that you find.
1B. Requiring years of experience in technology that didn’t exist
Take a look at this job posting:

Besides the grammatical errors, what stands out in the previous ad:
- Big data for machine learning in order to solve large-scale applications didn’t exist 10 years ago.

What stands out in this ad:
- TensorFlow was released on November 19, 2015, approximately 5 years and 5 months before this job was posted.
- PyTorch was released in September 2015, approximately 5 years and 7 months before this job was posted.
Recruiters are routinely asked for data scientists with 8–10 years of experience in data science but data science wasn’t a thing that long ago.
1C. The live coding part of the interview

The following text is adapted _from Daniel Kahneman’s Thinking Fast and Slow:_
Imagine you are being watched by five strangers examining you. Judging you.
Now quickly,
A bat and ball cost $1.10. The bat costs one dollar more than the ball. How much does the ball cost?
Kahneman explains that if you answered 10 cents, that you answered this question intuitively, and that along with 50% of students at Harvard, Princeton and MIT, you got it wrong. Studies show that under stress, we make intuitive decisions, rather than deliberative ones (Yu, 2016). Interestingly enough, if you tell yourself that you’re awesome before taking a live python coding exam, you should perform better on it (Creswell et al., 2013).
People who perform well made deliberative decisions under stress during the 45 minutes that you talked to them on that day.
This skill is particularly important for:
- Race car drivers
- Scuba divers
- Astronauts
People who don’t do well on a live coding test may make more deliberative decisions when they can relax and think about them properly. Unless you want to weed out a large number of analytically minded people, you might think about looking for evidence for good problem solving abilities. If you’re hiring for a data scientist role, try evaluating their performance on a 24 hour take-home project. If you’re worried about cheating, studies show that putting on an honor code greatly reduces this.
Also, the ball costs 5 cents.
2. Not budgeting for the role
Using personal anecdotes instead of market data
No: My friend is a hiring Manager at Company X, and they just hired somebody that makes 15 jelly beans a year, so that’s what I’m going to pay too.
Yes: Find a profile of what kind of employee you want, and base your budget on a multi-year record of what that person makes in your market. A recruiter should provide this information for free.
One company was interested in a senior data scientist and had found the perfect candidate, but the potential hire wanted 20 jelly beans more than what was budgeted. Because. Hello. They are a data scientist. They knew their market worth. After all, Glassdoor and LinkedIn provide salary information. Supporting data showed 3–4 profiles that also suggested that the budget was too low.
Finally, the hiring manager got approval to raise the offer, which took 1.5 weeks. On the fourth day, the candidate accepted another position at a competitor (at an even higher rate), was very successful and still works there 4 years later today. If the hiring manager had consulted real-time market data before creating the role, they wouldn’t have lost their preferred candidate to their competitor, and they had to raise it anyway to hire the next candidate.
3. Lack of feedback
Not telling the recruiter what went wrong
Often hiring managers write a job and advertise it expecting to be flooded with resumes from which they can cherry pick the perfect candidate. What ends up happening is that Recruiters will forward people that don’t necessarily fit the role and nobody ends up getting what they need.
Why? Because the recruiter isn’t a software engineer/subsea diver/data scientist/robotics specialist/rotary engine mechanic. Sometimes they need your expertise to help guide them on bring you the best candidates. Not telling them what went wrong in the process is a missed opportunity to educate them in what they could improve. Building a relationship will get you better results.
For example, in one engineering role, the company was looking for someone who had experience in a very specific mechanism. The recruiter sent him people with similar experience but one after another, each candidate was rejected. After a feedback session, the recruiter understood what the hiring manager was looking for and got his creative juices flowing. He went to the automotive industry and found someone with the experience in exactly the same mechanism the hiring manager was looking for in an applicant pool that neither had considered looking in before. The company hired him and he’s still there 11 years later, now as a manager running his own team.
In conclusion
You have a big responsibility to find talent to fill your company’s human resourcing needs. The following tips will help you wade through the muck and find the jewels.
- Set realistic and attainable criteria for new hires. Be willing to adjust your expectations and keep an open mind.
- Hire the right people at the right cost. It’s faster and easier for both you and the candidate.
- Build a relationship with a recruiter to let them know how they can improve the candidates that they bring to the table. In the end, everybody’s satisfied.
Happy hiring!
Sources
Capital One, Director of Data Science job posting: https://www.capitalonecareers.com/job/mclean/director-data-science/1732/17587251?utm_campaign=google_jobs_apply&utm_source=google_jobs_apply&utm_medium=organic. Accessed on March 24, 2021.
Creswell, J.D., Dutcher, J.M., Klein, W.M., Harris, P.R. and Levine, J.M., 2013. Self-affirmation improves problem-solving under stress. PloS one, 8(5), p.e62593.
Hill, Jeremy. Personal communication, 2021.
Intel, Lead data scientist job posting: https://intel.wd1.myworkdayjobs.com/External/job/India-Bangalore/Lead-Data-Scientist_JR0161659-1. Accessed on April 8, 2021.
Kahneman, Daniel. Thinking, fast and slow. Macmillan, 2011.
Miller, Christian B. "Just How Dishonest Are Most Students?." The New York Times. November 13, 2020. Accessed April 8, 2021. https://www.nytimes.com/2020/11/13/opinion/sunday/online-learning-cheating.html#:~:text=Empirical%20research%20has%20repeatedly%20found,with%20schools%20that%20are%20not.&text=A%20few%20schools%20start%20the,to%20uphold%20the%20school’s%20code.
Sarkar, Dipanjan. Personal communication, 2021.
Yu, R., 2016. Stress potentiates decision biases: A stress induced deliberation-to-intuition (SIDI) model. Neurobiology of stress, 3, pp.83–95.