# Recruiting in a rush? Read on!

Updated: Nov 20, 2020

__Optimal stopping__ increases your chances of finding the best applicant. And you don't even need to interview everyone. Our story describes how a little know-how can go a long way!

Data & artificial intelligence seem to be high on every CIOs wish list. Data scientists and associated roles are literally worth their weight in gold. A quick search on __Google Trends__ shows there has never been a greater interest than now (please see Figure 1).

The key is to act quickly, find people with potential and improve their capabilities over time. As __Winston Churchill__ said, “Perfection is the enemy of progress”. It’s a simple concept but in a world where demand significantly exceeds supply — prime mover advantage is at a premium. This was certainly the case for Neural Notions Ltd.

## Building capability at Neural Notions

Neural Notions Ltd is a new start-up company focused on finding the best consumer products at the right prices. The idea is to build a recommendation engine that predicts consumers’ buying habits. It’s a well trodden path, but there’s plenty of room for improvement.

At least that’s what Mark Denman, the CEO at Neural Notions, thought. He had convinced his investors that this was a golden opportunity. “The last piece of the puzzle is hiring a chief __data scientist__”, he said, as he ticked the final box on his hastily scribbled whiteboard list. Mark nodded confidently to his audience, and noticed his reflection in the office window, was doing exactly the same.

It was hard to know if Emily’s grimace was due to her coffee or Mark’s request. “Mark — you’ve had my recruitment report for two weeks”, she said, still eying her coffee suspiciously. “Right now, finding a good data scientist is harder than finding a golf ball in a blizzard”. Emily was right, but she wasn’t giving up easily. Her tenacity and optimism were two of the many reasons Mark had married her 11 years ago. They had first met at Oxford university where she was studying for a master’s degree in economics.

The next morning, Emily grabbed Mark and declared, “Hireathon!”. Data scientists, she’d found, have easily damaged egos. If not hired on the spot, they will simply wait for the next headhunter to offer them careers with almost limitless potential. If the right person comes along, hire them immediately. Otherwise they are gone forever.

As she described this to Mark, she explained how someone else had obviously arrived at the same conclusion. “A national recruitment firm is running a speed dating event — for data scientists”, she exclaimed. “I would like to register us and I really want you to be there”. Emily easily handled his initial objections, “How does it work?”, he asked submissively. Knowing all too well that resistance was futile.

## What on earth is a hireathon?

“A hireathon is a little like speed dating”, Emily described. “As a recruiter, we have a limited time with… let me check… 24 applicants. There is one key constraint”, she added — “demand is far greater than supply. If we find the right person, we must hire them immediately or we can assume they are gone forever”.

Emily summarised the conditions of the event for Mark:

There are 24 talented data scientists that have agreed to see us over the next four days.

We have at most, one hour to interview each applicant.

We can only rank each applicant with respect to the other candidates we have already interviewed.

We will assign each applicant a number that defines how we rank them from 1 to 24. Lower numbers mean higher relative rankings — ideally we want to hire candidate number 1.

We can change people’s rankings at any time during the interviews.

We will know nothing about formal qualifications. We can only relate each of the interviewees to each other.

The interviews will take place in a random order, dictated by the organiser.

After each interview, we must hire the applicant immediately or accept they are gone forever.

“There are some other condition that may interest you”, she described. “But I’ll explain them over your favourite dessert, I’ll just go and check how it’s coming along”. Dessert was apple pie and cream, simple and appreciated. Emily served it up along with a few diagrams she had put together a little earlier. Mark shared his time between the pie and Emily’s drawings — although the pie was certainly getting far more attention.

“Enjoy your pudding first darling”, Emily said, clearly noticing his priority. “I’ll talk to you a little later”.

## Optimal stopping to the rescue

Mark trusted his wife — if not for her, he would not have started his current venture. He also tortured himself about the promises he’d made to his investors. They were not intentionally inflated — but perhaps a little optimistic. He asked Emily to describe her diagrams to him.

“It’s based on a theory called __optimal stopping__”, she said excitedly. “It’s a subject I studied at university after refusing your offer to bunk off for the last 2 weeks of my first term”. She didn’t add how tough a decision it had actually been. Mark had adorned the apartment they shared for 2 years with photos of his road trip along the west coast of the U.S. While she stayed at home, creating lecture notes for both of them. As interesting as __optimal stopping__ was, it could not quite match the splendour of a Pacific sunset across the Santa Monica horizon.

Figure 2 shows how it works at a high level. No matter how good someone is during the look phase, we cannot hire them.

“We start with a pool of 24 applicants, let me take you through one possible sequence of events”, she explained. She slowly nudged her first diagram (Figure 3) under Mark’s nose. For demonstration purposes, she had assigned a relative ranking to each applicant. “Of course, we can’t rank any applicant before seeing them”, she said. “But this will help to explain the process”. Emily went on to describe the features of a ‘look before you leap’ strategy. She continued, “We look without hiring anyone for a certain period of time. After this point, we hire the first candidate that is better than anyone we saw during the look phase”.

Emily pointed out the obvious implication of a look before you leap strategy.

“Of course, we’re in trouble if any of the applicants find out what we’re doing.

It’s unlikely they’ll turn up for interview if they find they’re part of the look phase.” On reflection, Mark felt this balanced their own constraint of having to offer a role immediately after an interview had finished.

Emily’s second diagram (Figure 4) showed their position after interviewing the first 9 applicants. At this point in time, the fourth application was the best they had seen. Predicting Mark’s question, Emily explained, “Nine applicants equates to an important and fascinating number”.

For this type of challenge, the look phase always equates to 37% of the total number of applicants. This gives us the greatest chance of finding the best applicant. Using this strategy, we will find the best applicant 37% of the time. It’s a mathematical quirk that the size of the look phase and the chance of success turn out to be the same number.

“Our best possible strategy only gives us a 37% chance of success?”, Mark asked. He felt a little deflated by the odds. This kind of news could transform an excellent dessert into indigestion — and that’s the last thing he needed. Emily comforted him and explained it was way better than a random choice of a little over 4% (that is, the odds of finding the best candidate by randomly choosing someone from a group of 24 people).

Following the process through, Emily presented Mark with her third and final diagram (Figure 5). “So now, we enter the leap phase”, she explained. “For our scenario, candidate 15 has an overall ranking of 2nd. If this was happening now, we would simply know that this is the best candidate so far and hire him or her immediately”.

Mark interrupted, “But the best possible candidate is 17th — we would not even get to see him or her at all?”, he half asked and half stated. “That’s true, love”, Emily replied. “Furthermore, if we interviewed the highest ranked candidate during the look phase – we would end up interviewing everyone. In which case we’d offer the job to the last candidate ranked 10th”. “Either that or walk away without a chief data scientist”, Mark added, not fully convinced of their approach.

## All’s well that ends well

Mark and Emily debated their position for most of the evening. It seemed that, given the constraints they were facing, the ‘look before you leap’ strategy really was their best option. Ironically, this became clearer after a second glass of wine — it had been a long and rewarding day.

Emily registered Neural Notions for the __Hireathon__ and they did recruit a new chief data scientist. It turned out to be the 12th person they interviewed on the day. In reality, they could never know where their new recruit ranked amongst all 24 potential employees. After all, they didn’t even meet the last 12 applicants. They only know that she was the best they had seen after the look phase had ended.

Neural Notions are enjoying their new adventures and Gemma, their chief data scientist turned out to be an excellent choice. She now has a team of Data & AI scientists and luckily, was not prone to the same constraints that Mark and Emily initially faced when recruiting her.

## It seems you really can hire great data scientists

Whilst our tale above is purely fictitious, the __optimal stopping__ strategy is real. This particular example is often known as the __secretary problem__. It’s believed that it first appeared in the February 1960 edition of __Scientific American__. Figure 5 illustrates the best approach given the constraints we defined.

## Real use cases for optimal stopping?

Despite our story, there are real world applications where __optimal stopping__ can help. Perhaps the best example is buying a property. If you dwell on an offer, there's usually someone close behind - ready to exploit your uncertainty. __Optimal stopping__ can tilt the odds in your favour.

As a seller, it provides a little mathematical rigour when you're questioning why on Earth you refused the last 5 offers. Biting your nails to the quick while wondering if anyone else will ever call. __Optimal stopping__ can't guarantee success, but it can definitely improve on an otherwise random process.

## A python simulation

There is an online simulation here: __https://obj-demo.herokuapp.com/__ that allows you to run a specified number of campaigns, each with a chosen number of applicants. Figure 6 shows the results for 300 recruitment campaigns, each with 200 applicants. We can see that the last applicant was never chosen (the option to hire the last applicant was not set). Also, the best applicant was chosen 37.3% of the time. This is in line with __expected results__.

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