3 Options for Coder Candidate Management

3 Options for Coder Management

As a recruiter, you spend most of your day interacting with technical talent.

To stay organized, your job involves a significant amount of recordkeeping. Tracking each candidate’s skills, qualifications, and professional experience can feel like a full-time endeavor. As candidates advance through the recruiting funnel, you encounter additional information, such as resumes, coding assessments, interview transcripts, internal notes, and emails.

Without a proactive plan for managing candidate information, it’s easy to feel overwhelmed. In this article, we’ll explore the pros and cons of three common approaches to candidate management.

1. Shared Spreadsheets

Does your company rely on spreadsheets to track candidate information. Don’t feel too bad — many firms still do.

Pros: Spreadsheets are attractive because they present minimal upfront friction to internal stakeholders (other than you, of course). Most everyone at your organization already uses spreadsheets, which bypasses the learning curve of a recruiting-specific application. In addition, spreadsheets are easily manipulated, exported, and customized, and Google sheets makes it easy for your colleagues to comment on the content.

Cons: As you know all too well, however, candidate tracking spreadsheets can’t possibly tell the entire story. Sure, you can do your best to keep excellent records and notes, but what about the details that can’t fit into a data cell (such as resumes and email threads)? It may be possible to link your spreadsheet to other resources and documents, but doing so fails to offer a cohesive view of each candidate.

2. Standalone Applicant Tracking Systems (ATS)

Given the shortcomings of spreadsheets, your company has likely considered the implementation of an applicant tracking system (ATS). Such systems deliver a variety of recruiting and administrative features, aimed at solving the headaches of spreadsheets and manual processes.

Pros: Many ATS systems are similar in structure to CRM (customer relationship management) software. The applicant is essentially a contact record, upon which additional information can be layered. Notes, attachments (i.e., resumes), and events (i.e., interviews) create added layers of transparency and accountability, which would otherwise be impossible with spreadsheets.

Cons: Implementing an ATS at your organization can be a big decision. There’s obviously the per-seat licensing expense, but there are also countless other opportunity costs to consider. Will users from engineering or senior management actually buy in? How much training is necessary? Will the system be too generic for the needs of recruiting technical talent? How will the ATS actually help fill the funnel — or will it end up being just another database of information? Answers can vary widely based on the ATS and your needs.

3. Skills-based Recruiting & Management Platform

Recently, a third option has emerged for technical recruiters like you. A coding-specific recruiting ecosystem, such as CodeSignal Recruiter platform, combines the tracking capabilities of an ATS with a community of 1 million pre-screened candidates.

Pros: In addition to helping you fill the funnel faster, CodeSignal Recruiter brings clarity to the entire candidate management process. The platform automatically organizes candidate assessments and interview histories in a highly intuitive way. An advanced collaborative coding environment makes it easier to facilitate interviews and objectively test candidates. And, if you’re already using an ATS, you’ll be delighted to know that  CodeSignal integrates to several popular solutions.

Cons: Skills-based recruiting is a relatively new concept and might require a change in how you think about recruiting.  As with anything that’s innovative, you may face some internal resistance. However, recruiters are seeing great results with skills-based recruiting so it’s becoming more popular. CodeSignal Recruiter isn’t free, but it is affordable and scalable.

Streamlined Candidate Management

Whether you rely on spreadsheets, an ATS, or an all-in-one platform, no candidate management process is ever perfect. Take time to continuously refine your workflow and seek out tools that align with your goals. Request a demo of CodeSignal Recruiter.

 

A New Generation of Skills-based Developers

skills-based software engineers

Over the past decade, we’ve witnessed a revolution in how information is accessed and consumed. From massive open online courses (MOOCs) to lectures published on YouTube.com, learning has been officially democratized.

As a result, talented individuals from across the globe are acquiring skills previously accessible only to those at expensive universities and learning institutions. Unfortunately, some firms have been slow to adapt their recruiting style to this reality, causing them to overlook an entire generation of coders.

On the other hand, a growing number of technical recruiters are embracing the skills-based movement. Unlike traditional resume-based recruiting, which is often riddled with biases and inaccurate assumptions, a skills-based approach can equalize the playing field for everyone.

Let’s take a closer look at why skills-based recruiting can be a win-win for any company hiring software engineers.

Skills vs. Fluff

Let’s face it — it’s not easy to measure an applicant’s technical competency for a skill. Sure, you have the candidate’s resume to go by, but how objective is that? To provide additional insight into the decision-making process, many companies will attempt to create their own assessments. Best intentions aside, such assessments can be difficult to administer and even more complicated to score fairly.

The ability to code is one of the areas where skill can be measured which is why skills-based recruiting is rising in popularity. Skills-based recruiting can help technical recruiters overcome resume fluff and hone in on candidates who possess the right mix of qualifications.  Within the CodeSignal community alone, there are more than 1 million pre-screened engineers who are actively engaged in building their skill profiles. Some skills-based platforms also offer integrated technical assessment capabilities, seeking to go beyond basic whiteboarding or algorithmic work.

Tapping into Hidden Talent

Another downside to traditional recruiting is its tendency to overlook those with nontraditional work histories. Take, for example, the stay-at-home mother who temporarily pauses her career to raise children. Although she continues to practice her skills every chance she gets, there’s little she can really do to impact her current work situation.

A skills-based model makes it possible for engineers with nontraditional work histories to knock down common employment barriers. From the employer’s standpoint, this can represent an exciting opportunity to tap into an underutilized source of talent.

“Elite” Shouldn’t Overshadow Skills

Most software engineers did not attend an elite technical university. In fact, some haven’t even earned formal degrees. Does this mean they’re unqualified by default? The sad truth is that many developers are ignored simply because they did not attend (or graduate from) an elite institution.

Skills-based recruiting overcomes this bias by starting with the developer’s technical know-how. Although educational experience may be relevant to the hiring decision, it should never overshadow a coder’s true abilities.

Empower Developers (& Your Company) with a Skills-Based Option

If you feel like you’re missing out on too many good developers, consider incorporating a skills-based option into your recruiting funnel. In doing so, you’ll add value to your company and further empower the new generation of developers.

Want to learn more?  Request a demo today!

CodeSignal Engineering Profile: Albina Ezus

There’s been a lot written about the gender gap in technology. The good news is that more and more companies are working to close the gender gap, and technology is helping as well.  We’ve had a lot of feedback from both recruiters and engineering teams that CodeFights’ skills-based recruiting has helped them source, measure and hire candidates in a much more objective way.

Albina Ezus is one of our rockstar software engineers at CodeFights. She came up with all the challenges for Python world which is one of the five worlds in the CodeFights Arcade, and also created the initial version of our testing and interviewing applications.  We wanted to share her story and recognize her contribution to CodeFights.

 

When did you know you want to be a software engineer?
Actually, when I was younger I wanted to be a doctor.   Then, when I was in high school I realized that I was good in math and that influenced my desire to become a software engineer. It wasn’t until University that I decided I wanted to code.

When I first got to university I did well on a test that put me in the ‘premier class’ and we started to code in that class.  A friend of mine made me join the ‘olympiad group’ which was an extracurricular group that met and worked on group coding exercises on weekends.  This helped accelerate my coding skills and I started to tutor other students in math and computer science.

 

How was your experience as a female engineer when you attend university?
As part of the ‘olympiad group’ I didn’t really notice any difference between myself and the male members of my group.  I did have to deal with a couple of ‘old school’ professors who didn’t want to spend a lot of time with female engineers.

 

How did you come to CodeFights?
Tigran (CEO) was looking for someone and knew my friend that ran the ‘olympiad group’ so that’s how I got connected.  I started working at CodeFights part time while I was still in university.

 

As a new coder what was your first project at CodeFights?
Initially when I came to CodeFights I worked on our content team – that is the team that creates the challenges for the engineers that come to CodeFights. As I built up my skill set, I started to work on automation projects and the CodeFights admin tool.

 

When did you take on the testing and interviewing applications?
I started working on the applications about a year ago.  It was really exciting because I was working on an application that our customers would see and use (as opposed to the back end application).  While building these applications, I had to do a lot of product and competitive research, as well as work with our UI designer; this exposed me to a lot of new experiences.


Building a new application is kind of a big deal, were you afraid of failure?
Not really, I was really excited about taking on the new project and the new things I would learn  and also share my ideas about the product. Since we had a long history of creating challenges for engineers on CodeFights, we had a lot of feedback from our developer community in terms of product design. This helped guide us in the initial stages and I worked pretty hard to get V1 of our application ready in 4-6 weeks.  Now, not only do lot of companies use our testing and interviewing tools as part of their recruiting process, we also use CodeFights internally to assess our engineering candidates. This way, we are continually improving and updating the application.

 

What advice would you give other women that want to get into software engineering?
If you love coding then go for it.  Don’t be afraid and remember that not everyone is threatening. The engineering community is pretty awesome — I’ve met some really supportive people. Regardless of the type of engineer you want to be, as long as you’re striving to be the best, you’ll get there. There are a lot of female engineering groups online and many of them can be very useful. Unfortunately, some members who publish their code online don’t have advanced programming skills, so they were the subject of discussions on Reddit and 4chan that used these coding samples as a basis to disparage women coders, and add biases against women in tech. At the end of the day, whether you’re a man or a woman you need to be a good coder – so it all comes down to the skills. Keep learning, practice, and let the work speak for itself.

How to Swim with the Big Fish (When You’re Not One)

How to swim with the big fish when you're not one

You work for a tech startup that has both an innovative product and an impressive story.

Since the company’s founding a couple years ago, the sales team has significantly grown the user base. Revenue is up, and customers are requesting new features. So much so, in fact, that you’ve had to quickly ramp up hiring for software engineers.

Despite the many positive trends, one thing remains true: Your company is still a small fish in a gigantic pond. And, to complicate matters, the big fish that you’re swimming with have equally large appetites, particularly when it comes to recruiting technical talent.

Does this mean your company must settle for second-rate talent that’s been rejected by bigger fish?

Absolutely not.

It does mean, however, that you must continuously refine your outreach program and expand your search beyond the traditional talent pools.

In this post, we’ll share a few tips for swimming with the big fish – when you’re not one.

Stop Dwelling on Your Company’s Weaknesses

When comparing yourself to larger tech firms, it’s easy to identify potentially insurmountable shortcomings. As a recruiter for a tech startup, you’ve probably wrestled with how to best address one or more of the following:

  • Completely (or partially) distributed workforce
  • Lack of a true “headquarters” or central office
  • Dozens of employees, rather than hundreds or thousands
  • Lack of a formalized “company culture”
  • Informal or semi-informal corporate hierarchy
  • Underdeveloped feature stack, as compared to competitor offerings
  • Minimal number of years in business

Without the right context, these challenges can seem difficult (if not impossible) to overcome. After all, most applicants want to be assured that they’ll be working for a well-established, solvent company. This concern causes many recruiters in your shoes to overcompensate, thereby attempting to position their companies as larger — and, as a result, more rigid — than they truly are. Development engineers are smart people, which is why they can see past such veiled attempts to gloss over the truth. (It’s also why so few of them reply to unsolicited outreach campaigns from technical recruiters!)

Your Weaknesses Might Actually be Strengths

Although you can’t change reality, you may be able to convert your weaknesses into an advantage. Remember, not every developer wants to work in a “normal” office setting and live in Silicon Valley. Not every developer wants employment with a multi-billion dollar mega corporation, either.  

As you reconsider your developer outreach process, consider how your company might differentiate itself by simply emphasizing the following possibilities:

Remote Work: Don’t underestimate the appeal of being able to work remotely. Developers love the flexibility of working from home or from co-working spaces.

Flexible Work Arrangements: Some developers might not be ready for a full-time commitment. Others might prefer working late hours. Flexible work arrangements can be a big draw, especially in the development world.

Less Red Tape: Your company doesn’t have a ton of mid-level managers overseeing every minute coding decision. That’s actually a perk for the result-oriented software engineer.

Making an Impact: The developers you’re recruiting won’t be lost in a ridiculously complex organizational chart. They won’t be doing menial work, either. You’re looking for high-caliber technical talent that can make an impact on day one. Use this to your advantage!

Career Advancement: Given your relatively flat organizational structure, the possibility of career advancement is feasible. Developers who join your team now could be getting in at the ground floor of something big.

Spend time crafting outreach messages that maintain a personal tone and incorporate your company’s key differentiators. In an age when developers are routinely spammed with canned messaging, taking a more organic, straightforward approach might give you the upper hand. What makes you different makes you unique, so just be yourself.

Expand the Search to New Waters

There’s no question that the big fish are attracting talent from all the obvious places. Job boards, developer groups, and LinkedIn.com are teeming with technical recruiters. Though you can’t overlook such sources of talent, you can try expanding your search to new waters.

One such “talent pool” is the CodeSignal Recruiter platform. Unlike traditional sources, CodeSignal starts with over 1 million tested and vetted engineers. At its core, CodeSignal is a community of developers seeking to better themselves through participation in interview practice and real world company challenges. As participants compete and engage through the platform, CodeSignal evaluates the participant’s efficiency of code, the time it takes to complete challenges, and the accuracy of the solution.

As a recruiter, you dive into this innovative talent pool by utilizing the CodeSignal Recruiter. CodeSignal Recruiter harnesses the power of machine learning to inform its proprietary candidate matching algorithm, and then adds a human touch to verify the algorithm recommendations to match the engineer’s preferences with your company’s requirements.  For example, if the candidate only wants to work at an enterprise company, then CodeSignal won’t submit that candidate to you even though the algorithm may identify that candidate as a technical match for your position. Unlike traditional passive recruiting, which relies heavily on the manual outreach process, CodeSignal brings the candidates to you that have signaled they are looking for a new position so the candidates are far more responsive.

In fact, recruiters who use the CodeSignal system enjoy a much higher candidate response rate than those who don’t — 5x the typical response rate of passive candidates, to be precise. Instead of making your pitch to hundreds of developers, you can focus on actually engaging a select list of coding experts.

Let the big fish swim in deep oceans. You’ve got CodeSignal now.

Get Swimmin’

Ready to jump in and give CodeSignal Recruiter a try?

Learn more about how CodeSignal can help your company recruit, assess, and interview technical talent. Get to know our affordable pricing plans and enjoy unlimited seat licenses and no caps on usage.

Guest Post: AI Will Dominate Recruiting – So Prepare For Major Changes In These Areas

AI Will Dominate Recruiting – So Prepare For Major Changes In These Areas

Most recruiters are busy with their day-to-day work. So, some fail to realize that many recruiting processes and tools currently in use will soon improve significantly by the continual learning provided by Artificial Intelligence (AI). In addition, not only will AI and its advanced cousin Machine Learning (ML) make recruiting processes faster and cheaper, soon and in many cases are already adding significant new capabilities that were simply not possible with legacy systems. However, relax, this isn’t a job security issue, it’s an opportunity to improve performance with little effort on the recruiter’s part.

It’s quite common these days for the CEO’s from Amazon, Google, MS, Facebook and Apple to expound on how artificial intelligence and machine learning will dominate their businesses over the next few years. Even Vladimir Putin stated, “The country that leads in artificial intelligence will lead the world.” It’s also important to realize that in addition to contributing to the most visible product areas, like digital assistants and driverless cars, “Machine learning and AI are a horizontal enabling layer” says, Jeff Bezos of Amazon, meaning that AI will impact and improve every major function and its processes and decisions. Recruiting leaders shouldn’t be surprised that I predict that “machine learning will soon begin to dominate every major aspect of recruiting.” Just as previous technologies like ATS’s and CRM’s have already transformed recruiting. It’s important for recruiters to be aware that there is an upcoming wave of mostly vendor developed recruiting applications that assist in producing extraordinary hiring results because they include machine learning capabilities.

The goal of this article is to highlight the upcoming AI/ML and technology changes that are likely to occur in each of the major areas of recruiting.

 

The Top 15 Recruiting Areas That Will Be Most Impacted By AI And Machine Learning

The areas of skills-based recruiting and job/candidate matching that will be impacted are below. Note that they are listed so that the initial items in the recruiting process appear first.

Recruiting areas related to finding and attracting prospects

  • Advertising placement and content – Machine learning will continually improve your placement process for branding materials and job postings rather than relying on costly trial and error approach to advertising. This is critical because accurate placement is essential if you expect to get the right kind and number of applicants. Systems will continually learn by analyzing visitor cookies and response rates so that you place your highly targeted materials in front of the right people at the right time. Also, machine learning technology can help you continually refine your content so that it gets the highest response from your recruiting targets.
  • Your own website and social media – continually improve by firms using machine learning on their web and social media pages to better attract and continually engage your target audience. Software bolstered with machine learning will also be able to monitor and make you aware of both positive and negative comments that others make about your firm and jobs on the Internet and social media.
  • Finding individual prospects – during sourcing will become much more automated and accurate when augmented with machine learning capabilities. Automated sourcing programs will be able to find many more and better matches, based on the continually updated target profile that you develop as a result of feedback. There are already vendor packages that allow you to identify currently employed individuals (e., passives) that are likely to quit soon and prospects that are likely to be diverse.
  • Enhancing prospect profiles – can make the existing candidate profiles found on sites (like LinkedIn) more complete by supplementing them with additional information that a machine learning program will find on the Internet. Machine learning driven programs can sort through a prospects search histories, cookies and social media sharing. The additional information on a prospects interest, capabilities and behaviors might indicate that a candidate can do things that they haven’t done in the past. Once they apply, chatbots can contact an applicant directly to clarify unclear elements in their resume or profile.
  • Improving job descriptions and postings Recent research data has revealed that job descriptions and job postings can be dramatically improved so that the content better attracts your target audience. So, rewriting them can reduce terms that create a bias. Software can now help you reduce those biases and add content that draws initial attention and that attracts more qualified applicants.
  • Responding to questions – from potential or actual applicants is immensely time-consuming for recruiters. So many firms are already utilizing chatbot’s to answer questions quickly 24/7. The U.S. Army, for example, has been using its Sgt. Star chatbot for over ten years to answer its extremely high volume of questions. Chatbots can also periodically update a candidate status, once again saving recruiters time.
  • Personalize selling – Machine learning uses big data to identify the attraction factors and the elements of the firm’s employee value proposition that best engage certain personas (e., types of individuals). Rather than a “one-size-fits-all” approach, this allows you to make your attraction, marketing messages and personal communications more effective because they are highly personalized to the individual.

Recruiting areas after candidates apply

  • Resume sorting – with machine learning software uses the resumes of successful hires at your firm to find patterns and then it can use these past success patterns as a basis for predicting which resumes and candidates are most likely also to be successful when hired. If programmed correctly, resume sorting software can also help to eliminate a great deal of unconscious bias in resume screening and candidate slate selection. Machine learning assisted search programs can also help you find hidden or lost talent within your ATS database.
  • Matching people and jobs – Using matching programs supplemented by machine learning can help a firm determine if there are any, less obvious, jobs that an applicant would also qualify. Matching people with jobs will also be improved by looking not just at an applicant’s past job titles and degrees, but also at their skills and capabilities.
  • Interview scheduling – is time-consuming and dramatically reduces your speed of hiring. Fortunately, there is existing software that allows a candidate to self-schedule their own interviews depending on their availability.
  • Interviews – can be time-consuming, so it makes sense to automate the initial ones with a chatbot that provides personalized questions based on your job profile. Also, there already exists technology that allows the use of neuroscience tools like voice and facial recognition to assess aspects of video recorded interviews that no humans could detect. There are even voice modulation programs that can help you obscure the voice of telephone interviewees so that it’s harder to identify their gender and national origin.
  • Supplemental candidate assessment – in addition to traditional interviews. Natural language processing can check language skills and online technical tests and challenges can help to assess the skills of applicants. There are automated programs that can more consistently determine cultural fit. Eventually, virtual reality simulations will be able to supplement interviews by giving candidates actual problems from the job to solve.
  • Offer acceptance – based on the candidate’s persona and profile. Recruiters can put together offers that are more likely to be accepted while at the same time treating all genders equally when it comes to compensation.
  • Learning from hiring failures – By definition, machine learning processes continually identify mistakes and errors. Recruiting will have an ongoing failure analysis process that continually and automatically finds hiring and bias errors and their root causes, allowing recruiting processes to improve at a much faster rate.
  • Other technologies – in addition to AI/ML technologies. Block Chain may eventually make checking educational and employment credentials easier and more accurate. Skype and video technologies already make it much easier to interview remote candidates without requiring them to travel. Machine learning will make predictive analytics in the area of projecting the future trajectory of finalists (in the areas of performance, retention and promotions) much more accurate.

Final Thoughts

Although most firms don’t track it, the average failure rate of new-hires at all job levels hovers around 50%. For example, Leadership IQ found that when “they tracked 20,000 new hires, 46% of them failed within 18 months”. Former Harvard Professor and author Michael Watkins reveals that “58% of the highest-priority hires, new executives hired from the outside, failing in their new position within 18 months”. Part of this broad failure results from overworked recruiters, normal human errors and unconscious biases throughout the recruiting process. Fortunately, the machine learning technologies highlighted above will soon minimize those problems through automation and continuous improvement. The results will be hiring faster, lower cost and more importantly hires that perform better on the job (i.e., quality of hire), that are more diverse and with fewer hiring failures. Recruiters should also take note that as more recruiting transactions are automated, it will allow current recruiters to “raise the bar” and to move into the more strategic Talent Advisor role.

Finally, recruiters should also be aware that they will soon be recruiting many more individuals into machine learning roles. The share of jobs requiring AI skills has grown 4.5 times since 2013 (Source: Stanford).

Want to see how machine learning can help you find better technical matches for your open roles?  Check out CodeSignal Recruiter or attend an upcoming webinar.

About the Author:

Dr. John Sullivan is an internationally known HR thought-leader from the Silicon Valley. Specializing in strategic Talent Management solution. He is a prolific author with over 900 articles and 10 books covering all areas of Talent Management. Fast Company called him the “Michael Jordan of Hiring”, Staffing.org called him “the father of HR metrics” and SHRM called him “One of the industries most respected strategists”. He was selected among HR’s “Top 10 Leading Thinkers” and was ranked #8 among the top 25 online influencers in Talent Management. Dr. Sullivan is currently a Professor of Management at San Francisco State

If this article stimulated your thinking and provided you with an accurate picture of the future of technology in recruiting, please take a minute to follow or connect with Dr. Sullivan on LinkedIn.

© Dr. John Sullivan 5/2/18 for Codefights