Pattern-based sourcing (PBS): How to Use Retrospective Information for the Company’s Best

23 May 2023

Pattern-based sourcing (PBS): How to Use Retrospective Information for the Company’s Best

Hire like-minded people

We often use the hyped expression “cultural fit”. In the same breath, do we understand our own company’s lifestyle, and can we determine if a person suits us or not?

Pattern-Based Sourcing (PBS) or sourcing based on the retrospective information of the company is the way to decrease the number of rejections based on culture fit. PBS is a framework to put into use the information about the current employees and lessen the number of rejections on the last stages of the employment process. Dean Da Costa is a prominent supporter of that method, I will try to elaborate on the topic and give practical use to the theory.


When thinking about PBS, the first hiring tool  that comes to mind is the referral program. If you want to find candidates similar to your team, it makes sense to ask your existing employees to be your recruiter. Make it clear for the team, explain why you approached them, point out what   you like about them and what kind of people you are looking for. If you skip that part and let people decide on their own you will end up with recklessly chosen candidates – your employees may enjoy their company, but they are not necessarily suitable for the position.


Active PBS

Before jumping to an active phase of PBS you need to put together as much information about your team as possible. Here is what exactly you need:

  1. Current role in the company
  2. Current responsibilities, stack, the grade
  3. Ex-employers
  4. Previous positions
  5. Education (grade)
  6. University
  7. Local / Relocation
  8. Source of hiring: how did we find that exact candidate?
  9. Social media profiles and accounts on the professional networks
  10. The exact date he/she joined the company

We will use this data to level up the sourcing game.

Step 1: Find people inside the company 

Consider team members who are engaged in the most similar activities. If it’s a new position or if you, for example, have never recruited people working on that stack look for people working on the same grade but in other departments. It’s important to find 3-5 people so that the sample is representative. Choose the best performers if the number of staff allows you.

Step 2: Accumulate information about those employees

Collect the information on the points listed above and look for patterns. Maybe, all of them are alumni of the same University. That’s rare, that is why look at their major. Maybe all of them studied Computer Science or Physics and decided to go with programming only after. Or all your best employees used to work at the financial companies at some point. Or maybe your best data scientists were analytics who later on found their interest in data science.

Step 3: Analyse the results

Not to spread the efforts in all directions possible, you can ask your target group about their social footprint. Make a detailed table and ask them to fill in all the info:

The channels are different for every position. If you are looking for a designer, don’t forget to mention Behance and Dribbble. Looking for a JS developer, include NPM.JS, while for data scientists the source is Kaggle.

Step 4. Start sourcing candidates at the chosen channels

The information we’ve collected will help with the first iteration – to identify  the most suitable candidates. And only after screening those profiles there is a point to move forward. It’s good to source from 25 up to 250 candidates for the first iteration. The number depends on the position and the location. In case you have sourced 500 and more candidates in the first iteration, that means you can  narrow your search by finding more patterns.

Let’s go through the cases and see how we can come up with the searching query for different positions:

> position: big data engineer with java AND kafka

> location: UK

> university: cambridge

> channel: linkedin

> string: linkedin university search string

> position: backend engineer with python

> location: Denmark

> university_major: “computer science”

> channel: linkedin

> special_note: not a freelancer

> linkedin x-ray string: OR -pub.dir “computer science” python (intitle:engineer OR intitle:developer OR intitle:programmer) -intitle:freelance -intitle:freelancer

> position: ui ux designer

> location: Germany

> channel: behance

> special_note: with programming skills

> behance x-ray string: “work experience” germany (“ui” OR “user interface”) (html OR css OR javascript OR frontend OR “front end”) -inurl:following -inurl:”collections_following” -inurl:appreciated -inurl:followers

> position: mobile engineer w/ swift

> location: Sweden

> channel: stack overflow, github

> special_note: high open source activities

> string: github top rank by git-awards, SoF users rating

Special Notes are the data we’ve retrieved from our employees, and are ready to use in sourcing new candidates.

Pattern-based sourcing and D&I

At first sight, PBS contradicts the principles of diversity and inclusion, as looking for like-minded people we can end up hiring look-alike people. All the developers will come from Cambridge, Oxford, and ETH Zurich. All of them we enjoy skiing in the winter with, watch TEDx and celebrate Christmas in the countryside.

PBS can be used for both look alike AND look un-alike demographics. As a theory, it is neutral in its core and the approach to use is to exclude several patterns so that different demographics can be targeted. Even if you see that the whole development team consists of guys aged 25-28, it doesn’t mean you can use that as guidance for future sourcing.

Kind of patterns you need to ignore:

  • Gender
  • Age
  • Origin
  • Physical features
  • Sexual orientation

If you explain to the sourcing team why you decided to use PBS and make clear instructions on how to prevent discrimination of the candidates, you will stay away from the problems.


Let’s sum up the keypoints:

– Search people inside the company

– Use referrals

– Don’t overvalue the company culture

– Diversity is cool

– Analysing your results can be helpful in future hiring process


This post is the result of a collaboration between Brainfood and Tatev, co-founder at

Meettal is a team of topnotch recruiters with a brand new approach for building tech teams. Along with recruiting, we create long-term relationships with candidates based on trust, fun and tech-love.