Home
/
Blog
/
DEI Hiring
/
In Conversation: Charles Rue, Head Of Talent Acquisition, IHS Markit

In Conversation: Charles Rue, Head Of Talent Acquisition, IHS Markit

Author
Kumari Trishya
Calendar Icon
September 13, 2021
Timer Icon
3 min read
Share

Hire IQ by HackerEarth is a new initiative in which we speak with recruiters, talent acquisition managers, and hiring managers from across the globe, and ask them pertinent questions on the issues that ail the tech recruiting world. For this first edition, we spoke with Charles Rue, Head of Talent Acquisition (EMEA), at IHS Markit. Diversity and inclusion are topics close to Charles’ and his work is a reflection of his efforts to make the tech world a bigger better place for coders of all backgrounds. So, it was a given that the topic of choice for this conversation would be related to DE&I.

Read on!

HackerEarth: Please tell us a bit about yourself and your journey in the hiring world.

Charles: After a previous life in management consulting, I started my career in recruitment in Japan, which is a great training ground for a recruiter as it is a market where professionals tend to be loyal to their employer, and are therefore extremely difficult to dislodge, especially for roles at foreign firms. After heading the Financial Services practice there for nine years, I relocated to Hong Kong where I successively set up a new desk for an executive search firm, opened the local office for a global recruitment firm specialized in Financial Services, and finally joined the recruitment function of a large global bank via their RPO partner. There, I gained considerable experience in large scale, complex recruitment campaigns, in areas such as Retail and Corporate Banking, Asset Management, Insurance and the full spectrum of Digital Transformation.

This last experience gave me the opportunity to later on join IHS Markit, a world leader in critical information, analytics and solutions, and head their EMEA recruitment function.

HackerEarth: As a talent acquisition leader, when did you start to understand the importance of creating diverse teams? Are there any real-life examples you can share with us?

Charles: At an early stage in my career, I was aware that diverse teams can tackle challenges much more effectively due to the richness of perspectives, especially in complex, changing environments. The real battle was convincing my clients when I was working on the agency side because their candidate assessment methods were not robustly documented or consistent. Assessment bias was rife, and what was expected from external recruitment agencies was essentially reinforcement, where interviewers and decision makers with already developed opinions were selectively incorporating information that supported their own views. Later, when I was in-house, it was easier to influence stakeholders.

I recall a specific example where our recruitment teams focused on restoring the gender balance of a financial services sales team. During the following year, work environment indicators went up, positive client feedback was more numerous, collaboration increased, and revenue went up. That small-scale example helped develop awareness among the leadership team.

HackerEarth: IHS Markit has been in the industry for a long while. Could you shed some light on how the hiring policies have changed/evolved at the company vis-a-vis DE&I?

Charles: Openness has been at the center of IHS Markit company culture. While Diversity, Equity and Inclusion have underpinned our corporate strategy and the way we want to hire and develop our people, we certainly have developed a more structured approach in recent years. For example, we have enhanced our list of D&I partners to help us better understand and connect with under-represented candidate pools.

Our recruiting tool combines artificial intelligence and neuroscience to assist in removing unconscious bias during screening. On top of that, we have developed our own internal interviewing framework called the IHS Markit Way to help ensure consistent interview questions and that everyone is being assessed against the same unbiased criteria by a diverse panel. Finally, on the Early Careers front, we have added D&I organizations SEO London and Wall Street Bound as our main candidate sourcing partners during our 2020-2021 Intern and Graduate recruiting campaign.

Also Read: How To Increase Your Diversity Hiring ROI

HackerEarth: What do you think are the top 3 mistakes that companies new to diversity hiring make when formulating policies?

Charles: There are quite a few pitfalls when looking at improving diversity in the workforce. The first one is not getting genuine support from the top leadership team. That’s paramount. Hiring Managers will sense quickly if the company’s diversity goals are hollow or if there are real consequences for not supporting diversity in every hiring decision. Leaders must be 100% committed to the company’s diversity objectives, and keep communicating about their commitment internally and externally.

The second pitfall is missing the data. Diversity data is the very first step before a situation can be understood, and corresponding diversity goals can be set. Not collecting the right data, and compiling the data in effective dashboards is like shooting in the dark. It will frustrate teams and slow down adoption. A third pitfall is not asking help from diversity professionals. I think it is a common mistake as most HR and Recruitment functions tend to think that tweaking policies and buying assessment tools will single-handedly drive a more diverse workforce.

This approach is totally missing the cornerstone of an effective diversity strategy: diversity attraction, which can be translated into ‘how to transform a company to make it really inclusive?’, and ‘how to connect with underrepresented populations, and develop the right role proposition that will lead to an application?’ This is where specialized organizations can provide guidance on inclusiveness, and also leverage their extensive network within underrepresented populations.

HackerEarth: A question that we love asking everybody: Skills vs. Diversity – which one would you choose and why?

Charles: I genuinely don’t think we should have to make this choice. We should aim for both. If we can’t find both in a given market, companies should then go for diversity and then develop programs that will create skills internally. This is what we are doing at IHS Markit through our Early Careers recruitment programs.

We partner with specialized organizations and make sure our hiring outcomes fully support our diversity goals. Candidates for Internships and Graduate positions are assessed using consistent methods, against four role profiles. We select candidates who exhibit specific attributes and show growth potential. Our cohorts are nurtured so that required skills can be grown, while all the time we never had to negotiate on diversity.

HackerEarth: Have you come across D&I initiatives from various companies that have wowed you, and why do you think they work? (Examples can be AirBnB’s WeAccept campaign, or Salesforce’s equality groups).

Charles: BlackRock has created many positive D&I initiatives including the organization of their MOSAIC employee network, or the use of a Rare Contextual Recruitment System for early career recruitment in the United Kingdom. The latter recognizes that not every candidate’s achievements look the same on paper. Using the Rare Contextual Recruitment System allows BlackRock to see beyond an online application to better understand the circumstances in which each applicant’s achievements have been gained.

From BlackRock’s perspective, this process enables the firm to identify the best talent from all backgrounds. Deutsche Bank also has done interesting things in the area of gender diversity. Deutsche has won an award for its global sponsorship program ATLAS, which helps women progress to senior positions.

HackerEarth: Google, Facebook, Apple, Microsoft, and Twitter decided to start publishing an annual diversity hiring report in 2014. That was the first time that tech companies publicly acknowledged the diversity gap in their workplaces and vowed to change hiring practices.

Seven years later, there is only a marginal increase in diversity numbers at these companies. In your opinion, what are these companies:

  1. Doing well
  2. Doing wrong and how can they better it

Charles: Clearly the situation has not improved much. I’ve read recently that the proportion of US technical employees (coders, engineers, and data scientists) at some of these firms who are black or Latinx hasn’t risen since 2014. It seems however that the proportion of women has progressed, though no company is close to parity yet.

On the ‘plus’ side, all of these firms have made large investments into various education programs to encourage more women and minorities to consider tech, to help address a legacy of underrepresentation. On the ‘minus’ side however, all of these firms are growing, and are in need of much more under-represented candidates than they used to be, while attrition for these very same under-represented populations is clearly much higher than average.

Basically, despite all their investments, tech companies still haven’t addressed biases in their cultures, promotion criteria, and the broader issue of inclusion and belonging. These items will need to be on their agenda if they want to make an impact on their own D&I goals.

HackerEarth: There is a lot of talk about data-driven recruiting. When it comes to diversity hiring, what are the metrics you think talent acquisition managers should live or die by?

Charles: Purely from a talent acquisition perspective, there should really be three diversity metrics:

  • the first one measuring whether proportions of job applicants are reflective of the local population’s diversity mix (gender, ethnicity, socio-economic background, sexual orientation, etc.),
  • the second measuring whether the same diversity mix is eventually hired locally,
  • and the third measuring whether retention levels are consistent across populations, including women, minorities or under-represented ethnicities.
All three metrics should be measured at country level, across role levels, and department, so that data aggregation does not hide a local diversity issue. These three metrics will uncover attraction gaps and hiring/promotion bias, and should lead to a more accountable diversity strategy.

HackerEarth: Let’s end this with a tip (or two) for recruiters/talent acquisition managers who would like to amp up diversity hiring in their companies..

Charles: First, talk to your firm’s top leadership team and secure their commitment to taking responsibility for building an inclusive hiring process. Leaders should communicate their commitment to the principles of Diversity to the rest of the firm.

Second, work with your HR Analytics team and start measuring team diversity ratios before setting achievable targets.

Third, take concrete action by writing inclusive Job Adverts (the Gender Decoder tool is free!), advertising job adverts on diversity friendly job boards, actively reaching out on LinkedIn to underrepresented candidates, and by assessing candidates using objective and consistent methods.

Fourth, talk to professional D&I organizations that will help you refine and structure your approach. They have seen it all, and will help save a lot of time.

About Charles Rue:

Charles brings with him a decade and a half of recruitment experience at notable companies like HSBC, Eames

Charles Rue, IHS Markit

Consulting, and the Michael Page Group. He has been with IHS Markit since 2019 and is a champion of diversity and inclusion in the tech space.

Charles has more than 16 years of recruitment experience in the EMEA and APAC region, developing an expertise in volume (Experienced and Graduate) and senior to executive level permanent hiring in the Banking, Data, Digital, Insurance, Fintech, Asset Management and Payment Solutions sectors. Prior to joining IHS Markit, Charles was responsible for the delivery of large recruitment volumes for HSBC in Hong Kong.

Charles has been involved in a broad range of recruitment performance improvement projects and D&I initiatives in various setups, from external recruitment agencies, to RPO and in-house environments.

Subscribe to The HackerEarth Blog

Get expert tips, hacks, and how-tos from the world of tech recruiting to stay on top of your hiring!

Author
Kumari Trishya
Calendar Icon
September 13, 2021
Timer Icon
3 min read
Share

Hire top tech talent with our recruitment platform

Access Free Demo
Related reads

Discover more articles

Gain insights to optimize your developer recruitment process.

Vibe Coding: Shaping the Future of Software

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing code.

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
Top Products

Explore HackerEarth’s top products for Hiring & Innovation

Discover powerful tools designed to streamline hiring, assess talent efficiently, and run seamless hackathons. Explore HackerEarth’s top products that help businesses innovate and grow.
Frame
Hackathons
Engage global developers through innovation
Arrow
Frame 2
Assessments
AI-driven advanced coding assessments
Arrow
Frame 3
FaceCode
Real-time code editor for effective coding interviews
Arrow
Frame 4
L & D
Tailored learning paths for continuous assessments
Arrow
Get A Free Demo