Remote Hiring – A Paradigm Shift

Remote Hiring

Remote Hiring


Covid-19 pandemic has pushed many companies beyond the tipping point to adopt remote working as the new normal. Remote working is quite easy and natural for the knowledge workers – especially those working in the field of software development. This article focuses on how remote working has impacted hiring software engineers and the demands that it puts on the hiring tool being used by the companies.

Accountability and transparency

Talent acquisition managers would no longer have their teams of recruiters next to them. They would feel disconnected and helpless due to the lack of visibility. This feeling would be even more acutely felt while working under time pressure. They will, on a daily basis, need to know what their teams of recruiters have been doing. There has to be a proper measure of performance. E.g. if a recruiter gathers a lot of missing information which results in rejection of the candidate, then the recruiter should be get recognized for saving the time of all the stake holders and for preventing a potential bad hire. Companies should use tools that have proper metrics to measure the performance of their remote teams. In addition to the conventional quantitative metrics such as number of candidates sourced, screened and tested, companies should focus on qualitative measures such as average suitability score. The tool must judiciously assign importance to various selection criteria while determining suitability.

Removing bias and getting feedback

Personal biases introduced by likes and dislikes get discovered in face to face interactions. Decisionmakers subconsciously apply corrections to arrive at an unbiased decision. We need to have a mechanism to remove biases and look at one version of truth in a remote team. A tool that has a metric associated with each selection criterion lends itself well for such a mechanism.

Such a tool would also allow companies to assign relative importance to these selection criteria by assigning weightages. The rationale behind assigning numerical weightages to these selection criteria while calculating the suitability score can be shared with the hiring managers. In fact, hiring managers should be encouraged to fine tune the requirements by refining these weightages. Changing weightages for a reason could be a way to capture and convert verbose feedback into meaningful numbers.

Technical Assessment

 Most companies administer technical tests to assess programming skills. Some of the companies are using on-line technical tests. The pandemic situation has made it virtually mandatory to conduct the technical assessments on-line. Technical assessment can be a quick test with a few MCQs or a grueling code sharing and pairing session. Given the time and attention needed by an expert interviewer to do the later, it might make sense to use the former as a filter. Mobile based gamified tech quiz can make it easy and light for the candidates. There is a bit of a resistance from the senior candidates to undergo online tests. One way to get them to agree is to explain the shortage of technical panels to do a deep dive coding session with each and every candidate. Most of them see the merit of a quick and light quiz as compared to a telephonic round by a non-technical or a semi-technical interviewer.

It is highly desirable for the tool to ensure full video proctoring to avoid cheating. Also the questions that get asked should be picked from a large question bank to avoid repetition. Also the tool should automatically delete questions that everyone can answer or those that no one can answer.

Overcommunication and Collaboration

There’s no watercooler or cafeteria in the virtual world. Information that used to flow unhindered through these social interactions needs to flow even more rapidly to compensate for the lack of attention and context.

There are many tools that capture the first level of information. E.g. A candidate was rejected in an interview or another candidate submitted his quiz. However it lacks the next level of detail or the color. Why was the candidate rejected? Was he not as good at SQL server as we had assessed? Did the other candidate score well? How long did he take to complete the quiz?

There are many questions which don’t get asked because of the social and physical distance. E.g. a TA Manager won’t bump into a hiring manager and casually ask how he is finding the quality of candidates? Or there is no opportunity for a recruiter to ask why a candidate was rejected? Companies should acquire a tool that proactively answers such questions. Every piece of information needs to be supported by next level of data- ready to provide the detail should someone ask. The tool must provide a reason why a candidate should be considered for a job. The tool must tell why the candidate who was top ranked suddenly went down. The tool must encourage the hiring managers proper feedback and advice to the rejected candidates.

Companies should acquire a hiring tool that captures important events and alerts all the interested stakeholders using popular channels like WhatsApp and Skype. It’s also important to make these events actionable by providing links from the message to the appropriate page in the application.

There are multiple people collaborating to get work done. The agency sponsoring the candidate, the hiring manager, competing candidates and the recruiter should be alerted when the candidate successfully completes and submits the technical quiz. The interviewee candidate, the recruiter and the agency would be interested when the interviewer submits her feedback. The prolific alerts for all events would keep all the stakeholders abreast of the situation.

Employer Branding

The hiring tool should more than make up for the absence of handshakes and smiles, by providing a world class candidate experience. E.g. even rejected candidates should get proper feedback and valuable career advice. There’s nothing more frustrating than lack of communication that follows the interview. Providing proper feedback with current standing of the candidature in terms of the rank would make your company look very professional and enhance the employer brand value in the larger candidate population.

Conclusion: What has changed and how hiring tools should adapt?

Here are some challenges that hiring tools will need to address

  • Remote interviews have already become the norm. Now companies need to get more information about the candidates from all possible channels to make up for the lack of face to face interaction.
  • Companies need to identify gaps in the information shared by the candidate in her resume and get all such missing information from the candidate
  • Remote hiring is not only about remote interviewing. Companies need to remotely collaborate with all the stake holders including the recruiters, talent acquisition managers, hiring managers, interviewers, vendors and candidates.
  • Our tool must present a consistent view of not only the jobs and applicants – but also the status of each applicant in terms of her suitability, rank and interest to all the stakeholders.
  • Stakeholders should find easy answers to questions like why candidate X is more suitable than candidate Y and what Y should do to improve her ranking. This will help them drive the process to accurately identify the suitable candidates to minimize waste and disappointment.
  • To make up for the lack of warmth afforded by in person interviews, Companies need to go an extra mile to provide not only proper feedback but also some advice to the rejected candidates.

You could request a demonstration of Rezoomex- a hiring tool that promises to address all these challenges


Model for determining compensation

There’s an elephant in the room- let’s build a pragmatic, objective and transparent compensation policy! PART 2/2

As stated in part 1, this is an experience report from the author’s company where a more pragmatic, objective and transparent model is being used to determine compensation. For most skills having a sufficient number of jobs and job-seekers, a market mechanism becomes operational. Ruling market rates are the most impartial and objective way to determine compensation.

Boundaries that define the “Job Market”

There are 4 factors that define “market” for the purposes of employee compensation:


  • Geography- Inflation rates vary from country to country depending on the economics of that country. Fast-growing economies such as China and India tend to have higher rates of inflation.
  • Skills- Certain skills tend to be more in demand. Recently “Data Science” skills are in demand, which has resulted in better compensation for Data Scientists


  • Experience- A professional becomes better at his job as he gains more experience. This trend tends to taper off after 7 years. At which point one can think of up-skilling to get a better job.  The graph above shows how compensation reaches a limit after a few years in a job. The only way to break out to the next compensation level is by up-skilling to acquire a better job.
  • Performance- If we do A/B/C grading for employee performance on the job, A graders tend to do significantly better and C graders tend to do significantly worse than the average.

How to determine “market” compensation

We can practically determine the ruling market compensation by taking a small randomly selected sample. The sample should be large enough so that it results in a bell-shaped frequency distribution. We sampled candidate data sourced from job portals, employee and agency referrals for certain job skills and experience ranges in certain cities. Please refer to one such frequency distribution in the graph below. We observed that most samples resulted in a slightly right-skewed bell shape for sample sizes as small as 50!

Model for determining compensation

Model for determining compensation

The compensation model

We used these frequency distributions to build a compensation model that passes the following tests –

  1. Can the model be shared with all employees to bring transparency and objectivity
  2. Can the model manage non-uniform expectations based on skills and experience?
  3. Can the model lend itself to hire talent by offering good pay hikes to the new hires, without upsetting the loyal incumbents?
  4. Can the model account for special rewards for the superstars, thus avoiding ad-hoc negotiations?

Our company conducts quarterly performance appraisals in which employees are appraised of their last quarter’s performance by their immediate superior in an hour-long meeting. They can openly discuss and understand what OKRs they need to achieve in the next quarter to achieve an improved grade. We have successfully managed employees’ expectations by clearly linking percentile of pay level with grade achieved in an appraisal. (Refer to the table above) E.g. All “B” graders are paid at 75th percentile. There is total transparency as any employee can study the frequency distribution based on her technology skill and experience level. She can then decide whether to achieve better pay by up-skilling to a hot technology or to build deeper expertise in the same technology. As this model is based on the frequency distributions it makes it much simpler to uniformly relate performance to percentiles across multiple experience levels and technology skills.

Here’s a link to the frequency distributions we have compiled based on samples taken from 28 technologies and 5 experience ranges. We have made this data accessible by writing a web-based application called Paywatch. Our company has been Paywatch for building our compensation model as described above. You may want to use this data which is updated on an annual basis as it’s open for all to use and provide feedback.

We have carefully drawn samples across various experience levels to compile the Paywatch frequency distributions. The report is categorized by technologies because hot technologies like AI and Big Data tend to have higher pay levels as compared to older technology categories like Java or Dotnet. Your company can be easily benchmarked for each of these categories and experience brackets. Companies can achieve transparency by using Paywatch to formulate a rational compensation model. The model can be made transparent as employees can verify the pay levels by using the Paywatch frequency distribution relevant to their technology skills and experience levels.


This model is broadly known to all employees in the company. Those recommending pay raises have been briefed in meetings and via e-mail. We have been using Paywatch for the past 3 years for determining employee compensation. We have quarterly appraisals. A few (less than 1%) employees request reviews of the raises recommended by their superiors. Though Paywatch is open to all, not many employees actually use it to cross-check.  This article is also an attempt to get our employees to use Payatch and provide their valuable feedback.

Initially, we observed that some managers were more lenient in grading and generous in recommending raises. These anomalies stood out as they were clearly violating the Paywatch frequency distribution. We had to educate those individuals by asking them to justify their actions in writing. Some of these justifications were not valid. E.g. 50% of the team members can’t be top 5% performers. When we shared the feedback with all the managers, it resulted in a common understanding of the model. This helped us to bring uniformity and remove any individual bias. In fact, the model works so smoothly that recent raise recommendations needed no moderation or intervention by the HR department. There is so much transparency that no one is afraid of discussing her pay with others. In fact, a glitch in our payroll software had resulted in many employees getting pay-slips of many of their colleagues which was very embarrassing for the accounts and the HR folks, but it didn’t result in any dissenting or attrition. There was no surprise for anyone there!

We use the same model for making offers to the new hires. That is the mechanism to keep us honest. The fact that most offers were accepted, and less than 1% of the offers were rejected on the grounds of low pay, validates that our model is well synchronized with the market. It also proves that we have successfully avoided having double standards in determining pay for the new hires vs the loyal incumbents. New hires are smoothly on-boarded and accepted by the incumbent teams as there is no secrecy about the pay offered. 

Is this model the silver bullet?

We aren’t suggesting this as a silver bullet to address all the issues related to determining employee compensation. This is a sincere attempt to take on this big challenge instead of turning a blind eye. This is just a modest beginning. Employees, HR, and top management have to engage in a meaningful open debate using popular formats like fishbowl or breakout groups to discuss this topic and come out with a solution that is acceptable to all in the given context. Only then would we reach a decision to accept-

  1. Solutions or partial solutions
  2. “Workaround” in the absence of a solution
  3. The overall agreement that there is no solution or “workaround” likely in the immediate future
  4. Long term changes that need to be implemented to avoid some of these problems

Is this the only way?

There are many consultants who would charge a fat fee to do a benchmarking study and recommend pay structures. Paid studies are good for getting a one time snapshot of pay levels – but it would be unviable to get your company benchmarked every year.

Popular platforms like Glassdoor and Payscale crowdsource the compensation data. Even if we assume that the information volunteered by the users is accurate, these platforms have no control over the sample – making it hard for them to draw statistical inferences.

Transparency is the foundation of good candidate experience- Part 3

Shroud of Secrecy

There is a general shroud of secrecy around the interview process , hiring decisions and compensation offered. When you don’t communicate, people tend to lose trust. No wonder a recent survey by Team Blind shows that 70% tech employees don’t trust HR. In Part 2 of this 3 part article we saw the lack of transparency in the early parts of the tech hiring funnel. Viz. Resume completeness, job description and technical screening. In this part we will take a peek at what the candidates think about the end of the funnel- technical interviews , hiring decisions and compensation offered. From the results of the survey, it will become clear that lack of communication leaves the candidates guessing and left to themselves they aren’t kind to the IT companies. Remember talent is the primary driver of revenue and profits in the IT industry.

Candidates impression 1: Buzzwords score well in the technical interviews.

Buzzwords & Presentation score well

Buzzwords & Presentation score well

In the same survey more than 70% candidates said that interviewers do go into details of past experience giving more importance to analytical skills and problem solving abilities. About 44% also say that the frequently face deep dive technical interviews involving hands on coding exercises. But an overwhelming 70% surprisingly felt that companies give more importance to presentation skills and ability to use buzzwords. Companies can easily correct this impression by communicating their rationale behind choosing candidate A over candidate B. This information is never explicitly captured in the hiring process.

Candidates impression 2: Technically weaker developers can do better.

Technically weaker candidates can do better

Technically weaker candidates can do better

There is a feeling among candidates that technical abilities don’t translate into higher pay. It could be because there is no formal measure of technical ability. Every candidate thinks that she is technically better than the others. This impression can be corrected if companies adopt and widely share a metric that allows them to justify why someone deserves better pay as compared to others with the same level of experience. Interviewers’ individual opinions lack objectivity. Often competing candidates aren’t interviewed by the same panels leading to further deterioration of objectivity. A metric of technical ability can help the companies to reach more rational and objective decisions. 

Candidates impression 3: Job hoppers get better deals.

Job hoppers do better than loyal incumbents

Job hoppers do better than loyal incumbents

Some candidates become proficient in their interview skills. There are several resources on the internet like Glassdoor which give fair insight into the types of questions that are likely to be asked for specific jobs in specific companies. As a result those who switch jobs more frequently tend to do better in their interviews. Loyal incumbents lag behind. Companies maintain confidentiality and secrecy about the negotiated and offered compensation. The loyal incumbents get upset when the secret becomes widely known . Companies should realize that employees tend to freely share their compensation numbers among themselves. You can’t expect well performing cohesive teams without this level of informal communication. Companies should reward loyalty by correcting their pay structure to match the levels ruling in the market. Companies can use services like paywatch for effective pay corrections.


There is no doubt that there are many problems with technical hiring that need to be fixed. Lack of transparency reflects general apathy of the IT companies towards what candidates think. There will be a few companies who will see this as an opportunity and fix the problem by becoming more progressive and communicative. Software such as the Rezoomex Assessment and Ranking System will help these companies to measure technical ability and set compensation levels based on hard data to bring objectivity to the decisions. May be these progressive companies will get a headstart over others.

Does Winner Take All in the Tech Resume Sourcing Game?

“ I don’t really search for a job. All I do is to update my profile on Naukri and I wait for the calls – and believe me , I get more calls than I need” – A Developer with 4 years experience.

There are several job boards or web-sites where companies can post the open positions. But candidates – particularly the good ones don’t really “search” – they passively wait to be found by a recruiter. So it can be safely concluded that a developer or a tester with less than 10 years experience would default to this model. I thought that “Naukri” is where most recruiters would go to search for prospective candidates.

To learn the recruiters’ story ; I conducted a survey. Here are some insights based on the responses received from 36 IT recruiters.

  • Not surprisingly almost 70% of the respondents were using Naukri as the main way to source resumes
Most Used Sources

Most Used Sources

  • The picture became even more skewed in favor of Naukri when I compared the frequency of use- 78% were using Naukri every day.
  • All other portals (Monster, Shine, Timesjobs etc.) even when combined together looked miserable. 63% of the respondents used those less than once a month!
Frequency of use

Frequency of use

  • Does this mean that recruiters are happy and given more time would stay with Naukri forever. Hardly! Over 90% of them felt that very soon they should start using other sources like Google X-ray search, Stack Overflow and Github.
Plan to use

Plan to use

  • Almost all of them felt the need to use more information available on social media channels like Facebook, LinkedIn and Twitter to get more information in addition to what is available in the resume.


Though the current picture is very skewed in favour of Naukri, most recruiters are wanting to try out Stack Overflow , LinkedIn, Github and job postings on social media. Winner will have to come up with some strategy to keep the recruiters permanently interested. Here are some irritants that the users are not happy about.

  • Price- They find the price charged by Naukri exorbitant. They would like to establish a more reasonable way of sharing candidate information
  • Integration with ATS and HRMS. Naukri resists any attempts to automatically invoke its servers to pull information. There are no APIs published for even paying users to allow their programs to access Naukri.
  • Naukri is trying to be everyything to everyone. It has its own ATS and hence its against its business interest to integrate with Proplesoft , Success Factors or Taleo.

The Startup Way- Take-aways from the Lean Startup Week 2017

The main highlight of the conference was the new book- “The Startup Way” authored by Eric Ries. It was released in the week preceding the conference and I was pleasantly surprised to be able to buy a copy from a bookshop in Hong Kong airport. I finished reading the book on the long flight to San Francisco and was all set to hear more about it from Eric himself. I was in for yet another surprise when as a part of the conference kit I got a hard bound copy of the book which many of us got signed by Eric.

The book significantly captures how a passionately motivated team trying to solve a problem under conditions of extreme uncertainty can apply the principles of “The Lean Startup” – even when the team is inside an institution such as a large corporation , Government or a social enterprise. The common idea is that any hypothesis can be validated by conducting a controlled experiment. These are low cost short iterations of few weeks each ending with success or failure. In either case the end result is that the team acquires some validated learning at the end of each iteration.

The Startup Way introduces some new concepts. Innovation boards are senior stake holders within the institution who act like VCs for the internal startups. They decide whether a proposed startup is worth investing in. Once the decision is made they hold the intrapreneurs accountable by reviewing progress on a regular basis in Pivot or Persevere meetings. The innovation board may decide to release the next round of funding or to kill the startup based on the value of validated learning by the startup. The board gives complete freedom to the startup in the way the funds are utilized. But the startup has the onus to use the funds judiciously so as to achieve demonstrable validated learning so that they can go back to the board for the next round. This way of funding is called as metered funding.

As one can see “accountability” is the foundation. The process is designed so that it builds the culture needed to motivate and empower people. All the words in italics have more detailed explanation in the book. The pyramid below shows the order of priority and significance associated with the four concepts.


Accountability is the foundation

In this earlier article I had discussed the problems with hierarchies and various options being evaluated. The discussion was inconclusive and we remained admitting that no single structure can replace the hierarchies. “Horses for courses” as suggested by Tathagat Varma was the summary of our conclusion.

The startup way seems to have taken this thought one step further by suggesting a perpetually transforming organization that spins up startups to deal with the challenges as they come up. Some startups will succeed and others will pivot or die. This dynamic structure behaves more like a tree and less like a tower.The process of transformation itself is a series of lean startup style experiments . The diagram below describes the org structure in more detail.


Organization Structure for the Startup Way

Startups can be nurtured within the project teams with ambitious and passionate individuals hiring team members and “moonlighting” their way to launch. At some point this team would get blessed by the “innovation board” with a round of metered funding. This means the money comes with strings attached. Innovation board would ensure survival of the fittest by continuing to fund only the few deserving startups. Finally the successful startup would become an integral part of the organization by becoming a revenue earning , profit making project team or business unit.

The entrepreneurship function is just like any other staff function such as marketing or finance. The functional head is in charge of a knowledgebase of validated learning gathered from failed experiments. She will also ensure that the innovation boards do their job of guiding and nurturing the intrapreneurs.

Everything that is written in the book is supported by actual work done in several large conglomerates like GE and Citibank. Its very hard to judge the level of success given the size of these organizations. Most of the evidence is anecdotal. “The Startup Way” brings hope to the large hierarchical dinosaurs – may be they would metamorphose into nimble and vastly more intelligent forms.

The biggest take-away from the book is the accountability that lies at the foundation of the transformation. It brings some sense of control to the whole process of experimentation which is likely to run amok in endless iterations. Measuring validated learning by monitoring a leading metric which serves as a proxy for the net present value is at the center of innovation accounting. The proxy metric also needs to be corrected by accounting for the probability of it actually resulting into a certain net present value. This makes it easy for the innovation board to do apples to apples comparison between various opportunities competing for the next round of metered funding.

Execution Behavior change Customer Impact Financial Impact
Did we do what we said we were going to do? Are our people working differently? Do customers (internal and external) recognize an improvement? Are we unlocking new sources of growth as a company?
Project Team Is the founder and the team committed and stable? Has the team spent enough moonlighting hours on baking the idea? Does the idea pass the test of basic commonsense? Are they having a well defined business plan canvas? Do they know their LOFA? Do they hold P & P meetings? Do they run their experiments properly? Is there a recorded impact in terms of customer satisfaction? Shorter cycle time? Monetary savings? Customer referrals? NPV of business model. Productivity gain? Savings?
Business Unit What is the % of employees in startups? % of startup founders in this BU? What are the number of successful projects in the BU? Does the BU have mechanism of celebrating failures and capturing the learning? Employee morale? Per project cost? Average time taken by projects before first customer demo? Average cost incurred before first customer demo? Growth of BU revenue? Billable Head Count? Total Valuation of all projects incubated by the the BU
Company What is the % of employees in startups? % of startup founders in the company? Success rate? Is everyone talking about LOFA , Experiments and NPV? Number of pitches to the innovation board? Is the process simple? Are we able to attract better talent? Is it impacting Net promoter score? Are the number of red days zero? % of deep green accounts? New products launched? Value of phantom stock, Growth in revnue , billable head count and EBITDA, % of resources on bench

The cells in the table suggest some leading metrics for each stage from execution to behavior change to customer impact to financial impact.

In the end I would like to leave the readers think about a quote from Jack Welch. “ “If the rate of change on the outside exceeds the rate of change on the inside, then the end is near.”