Great employees are the foundation of successful companies. But great talent is hard to come by. McKinsey’s Steven Hankin coined the term “war for talent” in 1997, and it continues to be true today. The competition has only gotten fiercer over the years, and while the recessionary conditions over the last year may suggest a surplus of talent in the market, getting the right talent is still difficult. A survey by Manpower Group* reveals that in 2023 nearly 4 out of 5 employers face difficulty in finding the right talent.
Hiring the right talent is hard
Finding the right people that not only pass your coding challenge but also match your company’s core values is not easy. Here are a few reasons why:
Unhelpful resumes: Most often, the resume is the first interface between the company and the potential employee. However, these resumes may lack the information or may even carry inaccurate information about the technical and soft skills of the candidate. Given these inconsistencies, hiring managers and talent specialists end up wasting precious hours going over hundreds (sometimes even thousands) of resumes.
Job requirements: A job description is an important tool in finding and onboarding the right talent. However, what a hiring manager has in their head may not be translated well into the job description, leading to disconnects and inefficiency in the hiring process.
Assessment process: Most hiring managers rely on interviews as the final part of the selection process. However, random or unstructured interviews can lead to random results that are a sheer waste of time.
The problem with job interviews
A research reveals that most job interviews are broken or useless. They often follow an unstructured approach that allows candidates to ease into the process and highlight their strengths. Such interviews lean on questions such as “What’s your greatest strength/weakness?” and rarely produce genuine or relevant responses. What’s more, candidates preparing for interviews tend to ace such clichéd questions.
A New York Times article* suggests that employers are better off asking behavioral and technical interview questions to prospective candidates. Questions that elicit past behavior tend to portend future behavior more accurately.
Another article suggests that having a more structured approach with the same set of questions for all candidates can improve the reliability of interviews and enhance prospects of job success.
Is there a solution?
Turing experts believe that having a system that collects proper representation and information on the following three fronts can help find the right candidate.
Accurate qualifications and competencies of job seekers
Actual job requirements aligned with reasonable expectations
Valid and reliable assessment of the fit between the job and the candidate
A limited or regional talent pool can increase the difficulty of finding the right talent in a fast and cost-efficient manner. This is why Turing built an AI-powered platform that uses 20,000+ ML data signals to source, vet, and manage world-class developers. Following a structured approach, AI supports Turing’s vetting process and is Turing’s solution to the hiring conundrum.
How does Turing’s innovation help in hiring?
Turing’s AI-based vetting system is meticulously designed and thorough in its process and delivers reliable results. In this video, Turing’s CEO Jonathan Siddharth explains how Turing uses AI to evaluate developers. Here’s a look at Turing’s vetting process:
Create a global talent pool
Create a global talent pool
Turing’s platform has a global talent pool of 2M+ software professionals and is continuously growing. Highly qualified and experienced software professionals are joining Turing because they love our model. We offer opportunities with 400+ companies including leading brands. Since most of our client engagements are for long-term projects with a minimum duration of 3 months, software professionals see the opportunity for career growth. Most importantly, professionals love that they only have to be vetted once per skill for a lifetime of opportunities.
Data-driven structured competency vetting
Data-driven structured competency vetting
Turing uses 20,000+ ML data signals to source and vets the developers across 100+ skills.
Deep developer profile
Deep developer profile
Based on the results of the vetting, Turing creates a deep developer profile for every candidate. This profile offers the client an in-depth understanding of the skills and competencies of the developer.
Data-driven real-time feedback
Data-driven real-time feedback
When customers update their job requirements on Turing’s platform, our system generates real-time feedback based on our vast global talent pool.
AI-matching with the right talent
AI-matching with the right talent
Turing’s Talent Cloud uses AI to match the right talent with the given job description. Our AI model is so thorough that we can fill the position in just 4 days.
Hire only on the condition of successful post-hire performance
Hire only on the condition of successful post-hire performance
While a candidate may do very well on the coding challenge and the technical interview, it’s difficult to assess whether they’ll perform well under pressure on the specific project a client is hiring for. Turing eliminates this issue with our 2-week, risk-free trial offer. If the company isn’t impressed with the Turing developer’s work within the first 2 weeks, they pay nothing—Turing will pay the developer for those 2 weeks instead. However, this situation rarely arises as Turing’s vetting process is rigorous and the talent quality is very high, which is reflected in our 97% retention rate.
But why should you trust Turing’s AI vetting?
The following competencies are required to succeed in a job:
Soft skills: Communication, collaboration, remote work Technical skills: Understanding of technical concepts, coding, and practical skills to solve technical problems
Turing’s AI vetting process uses progressive assessment determined by two main vectors:
Role types: Front-end engineer, Back-end engineer, etc.
Dominant tech stack: React, Node, Python, Java, etc.
Further, our AI vetting is split into four stages:
Stage 1: At this stage, our AI system tests the basic knowledge concepts associated with the role type and dominant tech stack. Our system has automated knowledge tests for languages and technologies.
Code traversing assesses the outcome of a particular code block.
Knowledge or concept associated with the particular language or technology is tested.
The system asks solution-based questions.
There is a dynamic score based on the difficulty level of the question, thereby helping assess the quality of the candidate more accurately.
There are 150+ automated tests for 10+ roles.
Stage 2: After passing stage 1, a candidate must pass the coding challenge associated with their specific tech stack. The automated coding challenge assesses the use of
Algorithms
Data structures
Best practices (efficiency and performance)
The candidate’s performance during this stage is recorded to see their practical skills such as testing and debugging, deploying code, and working with APIs.
Stage 3: Apart from the technical skills, our system also has structured questions that assess the candidate’s soft skills. This ensures that the candidate’s core values and people skills match those needed for the job.
Stage 4: Finally, our AI vetting system matches the job requirements analysis with the candidate’s skills to ensure a perfect match.
However, Turing’s vetting process does not end with automated vetting. We also conduct face-to-face structured interviews as a mechanism to validate our assessments and augment the vetting signals when needed. Our experts assess the soft skills, communication, and remote work readiness of the candidate, with special attention paid to assessing spoken English and technical communication skills. Finally, there is a work experience assessment to ensure that the candidate is a great fit for the job role.
AI drives Turing’s success in vetting and matching candidates. Our scalable and reliable ML platform is built on Vertex AI.
Developers:
Funnel optimization ensures high-quality developers for matching
The vetting engine gathers signals for developers’ proficiency
Automated fraud detection ensures quality and trust
Customers:
Lead scoring helps optimize marketing and ad spends
Model lifetime value for long-term growth
Dynamic demand and supply forecasting
Matching:
Search and recommender system to match developers with jobs
Ranking algorithms and interview success predictions
LLM/NLP for jobs and resume language understanding
Turing’s AI system has developed a virtuous cycle that helps it glean learnings from data and improve its process continuously. This means that you benefit even when your competitor hires with us. We constantly recalibrate our vetting by incorporating post-hire performance feedback. We test and evaluate everything to refresh and revise content whenever needed.
Turing’s structured and rigorous approach to its AI-supported vetting model has earned the trust and confidence of 400+ companies including leading brands like Johnson & Johnson, Pepsi, Disney, and Dell. Entrust your hiring mandate to Turing’s AI-powered Talent Cloud and reap the benefits.
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