AI in Recruitment : Is Mumbai closer to Delhi than Agra?

Jobseekers prefer to work closer home, their native town or their current location. They may also prefer specific locations because there are more job opportunities in that city. For example, Mumbai is a hub for financial services and Bangalore for IT jobs. That said, IT companies now have centers across all major metros and even in small cities like Indore, Jaipur, Trivandrum.

Jobseekers are willing to move from (say) Agra to Delhi, however, it is hard for an organization to convince anyone to move from Delhi to Agra. Charm of a large metropolis, with its educational, health, entertainment and modern lifestyle, is attracting talent towards larger cities. It has become a one way street.

As a recruiter (and hiring manager), when I look at a candidate, is he more like to move to Mumbai from Delhi? or will he prefer to move to a location near Delhi, say Agra? Often, geographical distance does not represent the user preferences. Unless there is some personal connect with a smaller town or incentives are offered with a promise for better location in the future, candidates are unwilling to move to smaller city or town. (Note - Agra is also developing very fast, preferences can change in the future).

Location is a simple "Yes" or "No", yet there are many variables which come into play in the Indian context. Some of the jobseekers want to live close to family and some away from it.  And preferences evolve as "the family" evolves and needs of the family change. A large number of jobseekers are willing to change location for the "better opportunity".

Location Preference Within a City
Yet, we see several employees depart because Gurgaon or Noida are too far from their current residence. Within a city, geographical distance or the daily commute is a major driver for employee satisfaction. An employee who was unhappy with his daily commute may eventually change the city itself (and not change his residence within the city).

AI Algorithm Must Understand the Preferences
The nuances of large and small city, distance within the city and also, personal preferences are all challenges for the AI algorithm to overcome.

- Vivek Jain

Please also see my blog post on (1) AI in Recruitment - Understanding Skills and Designations, (2) Story of Naukri Job Alerts, and (3) AI in Recruitment - Do Job Descriptions Represent the Intent of the Recruiter? 

Naukri RMS - Nominated for IDC AP Digital Transformation Awards 2017

Naukri RMS received the IDC India Digital Transformational Award last month.  Congratulations Naukri team and thanks IDC. Naukri RMS is the new age Recruitment Management System which automates the recruitment process end-to-end from Requisition to Offer.  With over 3000 customers is three years of its launch, Naukri RMS has become the leader in this space.

Naukri RMS (earlier known as Naukri CSM) has been nominated for the Regional Awards - IDC AP Digital Transformation Awards 2017.

For more details on IDC Digital Awards, please visit - IDC Digital Summit 2017

AI in Recruitment - Do Job Descriptions Represent the Intent of the Recruiter?

Job descriptions are essential part of recruitment. Once hiring manager creates a requisition and gets it approved, a recruiter will work with hiring manager to create a job description. A job description has dual purpose -

(1) it helps to attract jobseekers by pitching the unique attributes of the role for which recruiter is hiring, the reasons why a jobseeker will like to work in the advertised role, and

(2) it enables the recruiter to specify what kind of candidates she is looking for and also for jobseekers to know whether they are qualified for the requirement or not.

Job descriptions however may fail to deliver on the above two promise.

Recruiters may not have a job description to begin with, and they end up writing it with sketchy details on what a person is expected to do. Often the requirement evolves as the hiring manager and the recruiter meets jobseekers. Once the recruiting team knows what kind of skills are available and if no matching candidates for given set of requirements are found, hiring managers may modify their requirements.

Will recruiters update the job descriptions and re-advertise the positions with the new and updated job descriptions? Sometimes, yes and sometimes, no. If there are sufficient candidates available in the already received "applies", the recruiting team may decide to rely on the existing candidates and not re-advertise the updated requirements.

Now, if the job description is very well documented and the recruiter has already hired against the same position earlier, we can expect the job descriptions to represent the intent of the recruiter. That said, the AI algorithm is typically built on historic job descriptions and the response of the recruiters (in aggregate) to applies, hence, some of the "ambiguity" in the recruiter response is already embedded in the AI algorithm. This "ambiguity" may not always be helpful to the recruiter.

-Vivek Jain

Note- Even if job descriptions completely represent the intent of the recruiter, does the AI algorithm completely understand what is specified by the recruiter in the job description?

Please also see my blog post on (1) AI in Recruitment - Understanding Skills and Designations, and (2) Story of Naukri Job Alerts

AI in Recruitment - Understanding Designations and Skills

Relevance of jobs for candidates and candidates for recruiters is the most important challenge for AI in recruitment. Whether it is an Application Tracking System or a job portal, recruiters want easy mechanism to identify the most relevant candidate. That said, only a recruiter knows what she wants. The AI Algorithm only knows the job description which she shares with the system (there still exists a gap between what she wants and what the description says).

Over the last few years, this has been area of major focus and attention for our team at Naukri.com. I will discuss here on some elements which are important in solving this challenge.

Challenge 1: Complexity of Indian Economy - No one sector or Industry dominates

India is a large country with several 100 industries and sectors with companies of varying size. Every organization has many unique roles and designations that employees carry. Even within the organized sector, we have more than few 1000 roles and may be more than 50,000 designations. AI Algorithm needs to understand what each of the designations stand for.

Challenge 2: Creative Designations

Every organization is creative with designations and often internal designations are created to balance the organization challenges and individual aspirations. In many companies, Software Developers carry the designations like Software Engineer, SSE -1, SSE -2, Member of Technical Staff. However, few companies call their Quality Engineers as Software Engineers.

Often designations are created to represent evolving role descriptions based on the unique organization requirements. For example, few years ago, Mid-Office was created as a designation to distinguish teams from Front Office and Back Office. Similarly, we have seen new age professions emerge, for example, Digital Marketing, SEO Specialist, Social Media Marketing Manager, Data Scientist and so on.

For a system to understand the requirement, AI Algorithm must first understand the designations and the similar designations or related designations which other companies may have.

Challenge 3: Some Designations carry no information about role

Often designations are devoid of specific domains and also, role information. Some jobseekers write designations as Vice President, Manager, Senior Manager, Officer etc.

Challenge 4: Skills, Regions, Divisions are part of Designations

Skills are also part of designations which are often used to differentiate employees in the same role with specialized focus skills or areas of responsibility. For example, Software Developer, C++ Developer, Java Developer, Senior Engineer- COBOL and so on. In Sales function, we may have designations like Sales Regional Manager, Territory Manager - Bhopal, Area Sales Manager- Mangalore, Regional Manager - Paints and Specialty Chemicals etc. As we can observe, Cities and business units have been appended to these designations to differentiate sales managers playing similar role with special focus areas.

The challenge to disambiguate designations is not trivial as new designations are created on an ongoing basis. Skills are often used by jobseekers to distinguish themselves vis-a-vis other jobseekers.

AI algorithm needs a library of Designations & Skills and their inter-relationships. Have we solved the matching problem with regards to designations and skill sets? May be to a large extent. Yet there is scope of improvement and our effort continues. There are many other elements which play an important role in identifying relevant candidates, which I intend to talk about in later articles.

- Vivek Jain

Note - The challenge of overstated or understated skills is a conundrum which can only be solved by assessments. In my view, most jobseekers still faithfully represent what they know and what they don't know. And those who don't, are typically eliminated through the assessment process. Often an expert recruiter will look at signals beyond the stated skills, for example, the educational institution from which the jobseeker graduated or the company the jobseeker is working in.

Also see my blog post on Story of Naukri Job Alerts.

My Keynote Presentation at Data Science Conclave 2017 in Chennai

I am sharing my my keynote presentation at Data Science Conclave 2017 in Chennai. Thanks Rajesh for the invite.

1. Major improvements in accuracy in speech recognition and image recognition opens up a new field in human computer interaction. With computers able to correctly interpret almost all interactions without direct contact with keyboard or mouse, a major data source has opened up for Data Scientists to explore.
2. A system which is 80% accurate may not usable, however, when accuracy crosses 95%, there is a major turnaround in large scale adoption.
3. Self driving cars will lead to major leaps in technologies for object recognition -> not just previously known objects, also to anticipate and correctly handle unexpected objects.
4. In my view, there are four key dimensions of Data science, these are Data, Domain Expertise, Machine learning algorithms and Technology of Deployment. Value creation is possible across all the dimensions of Data Science. Better quality data, higher volume of relevant and contextual data can create value, and domain expertise remains critical in making successful deployments of data science projects. Our focus on machine learning algorithms is important, however, value creation happens across all the four dimensions.
5. We have seen a 5X increase in jobs which require machine learning and neural networks expertise.

Data Science is now mainstream and it is important for every organization to invest in Data Science and benefit from it.

https://www.slideshare.net/vjain99/data-science-conclave-keynote-presentation

Customer Insight - Survey, Data and Interviews

Customer insight is the only sustainable basis for building a business. And in my humble view, there are three ways of gathering customer insight, 1. Conduct a survey, 2. Analyze behavioural data, 3. Conduct in depth customer interviews.

There is a lot of behavioral data on the internet.  From browsing history to clicks and transactions. However, behavioural data only says "what" is the customer doing and when. "Why" is missing !!

Customer interviews can give insight about all that a customer cares about. The only challenge is that it covers only one customer at a time. Often, you may not have a large pool of customers to do indepth interviews, a lot of time may elapse between successive interviews or different customer context may make it hard to correlate insights across them. That's where surveys come into play.

Surveys enable quantitative measure of customer's opinion. If you have access to a large pool of customers, surveys are low cost and quick. Without surveys extrapolation of 1/few customer's opinion as the fact, is fraught with risks.

And without depth interviews, survey insight is shallow.

And without correlation of behavioural data, surveys and depth interviews are just opinions, which may not show up in reality.

If you are wondering on how to move from "I feel" to "I Know", use the three methods of gathering customer insight. You will know when you can say "I Know".

Digital Marketing and Relevance

For any brand, digital marketing is now mainstream. Ignoring digital is like ignoring reality and for a marketer, almost a professional suicide. When brick and mortar businesses like hotels and restaurants have to rely on digital marketing for business, no one can really remain untouched.  Any digital marketing initiative must examine relevance of message to the recipient, one it is possible and two, your business will cease to exist without it.
There are learnings we can derive from already established digital businesses. Take ecommerce companies for example, mailers drive traffic for major offers and seasonal discounts.
1. Capturing sufficient information to personalize the message - Relevance has multiple connotations, every person has unique set of requirements and aspirations. Without capturing this information relevance cannot be achieved.
2. Current and correct information - Capturing detailed information that is correct and updated is also an important challenge. Customers provide their location and interest in products and categories through their browsing or purchase behavior. It is obviously unrealistic to expect every declaration of interest to match with displayed behavior. Hence a strong need to constantly update the customer attributes.
3. Classification - Another tool for matching that is used very often is classification. For example, an ecommerce shopper expresses interest in bed sheets, now does that mean we can send offers on Garments or home furnishings. While classification helps find commonality between both parties, classification errors add to the complexity.
4. Error tolerant matching engine -
A vertical specific matching engine can build on the domain expertise present in the organization. To make it more robust for errors in data capture and classifications, matching engine may use behavioral information or the domain expertise.
Improving relevance finally becomes a function of improving all of the above. Do share your thoughts. Wish you success in your digital venture.