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    Using Data-driven Hiring to Edge out the Competition

    When times are good, and business is booming, companies can afford to make a few mistakes and sweep a few imperfections “under the rug.” And that’s okay. No process is perfect. However, when business slows down and it’s time for spring cleaning, what was swept under the rug comes to light.
    In other words, during periods of quick growth, companies tend to sacrifice quality of hire for speed. The effects of these decisions surface most clearly when the pace slows down. That can be a sobering moment for companies that stop and take stock of the decisions that worked for them and the ones that worked against them. Recruiting efficiency is an area that is quickly and clearly exposed when this happens. The inefficiencies and the lack — or absence — of sound hiring practices can be seen in cost per hire, turnover, and retraining costs.
    To find improvements in any process, businesses look at data.
    Data, data, everywhere
    We’re not talking about boiling the ocean, but there is meaningful information that can be gathered and put to use everywhere in the recruiting process. Hiring leaders who do not operate with this mindset leave money on the table, which again, is easy to measure in terms of increased cost per hire, decreasing retention, or unsustainable retraining costs.
    Without data measurement, organizations cannot optimize for “all-weather” efficiency.
    Smashfly CMO Lori Sylvia goes all in on the importance of measuring talent data when she says, “If you can’t measure it, it didn’t happen.”
    This is not a call to recruiters to build sophisticated data models, but rather to critically think about how data can help determine who they should be hiring for and how they can best appeal to them.
    Knowing that data is all around us, the question needed to make use of it is: “What data points are the most meaningful to me for this process?” Here are a few tips for recruiters — of all levels — to make leveraging data easy, impactful, and second nature.
    Ask yourself who fits into the talent pool for your business
    The last part is important here. Someone may check all the boxes for the job description and still not succeed at your organization. It can come down to various factors, like culture, level of training, the ability to multitask, or teamwork. Whatever the reason is, hiring success depends on going a level deeper into the candidate profile than the resume.
    Let’s go over an example where the goal was to reduce the number of conversations and increase the quality of conversations with candidates. Brendan Browne, VP of Global Talent Acquisition at LinkedIn, was looking for candidates to fill an engineering role. They took a quality-affinity approach that measured the candidate’s qualifications (their quality) and how highly they thought of the company (their affinity). The criteria for affinity included asking three yes/no questions:

    Do they follow the company?
    Do they share relevant content on their profile?
    Do they have a meaningful first-degree connection?

    Upon reaching out to candidates who ranked higher in affinity, the team experienced a 57% increase in the response rate.
    There was nothing highly technical about the process. It just came down to the team figuring out what data points from each candidate were meaningful to collect. It’s an easy exercise that can be applied across companies and roles.
    Take a microscope to your outreach
    Keep track of your messages. Recruiters shouldn’t shy away from testing new copy, subject lines, and time of day for their candidate outreach. It’s the most obvious yet overlooked metric to gauge the effectiveness of your outreach. Doing this enough will give you a sense of what tone is resonating most with your candidate pool.
    To have reliable data, one cardinal rule is to test one thing at a time. For example, measure how two different groups react to a different subject line or call-to-action alone rather than changing both at the same time.
    If your message has reached a point where you feel it is well and truly optimized and it’s still not meeting your goals, shift your focus to identify weak spots in the candidate journey. There may be moments where engagement is dropping off for enough candidates, signaling a trend to address with an alternative approach — and then measure the success of.
    Think about who else is talking to your dream candidate
    Chances are, the competition is also talking to the same candidates as you. Keeping tabs on competitor hiring activity can help inform your hiring strategy. Think about what the hiring experience is like for the candidate when they talk to you, versus the competition. Check out competitors’ job descriptions and ask yourself:

    How do they communicate the employer value proposition to prospective candidates?
    How candid are they about the salary and benefits they’re offering?
    How much of the company culture and company values shine through in the description?
    How easy or intuitive is the application process?
    Do they show the prospect genuine gratitude for their consideration?
    What would I look to improve in this experience?

    Doing this, even once in a while, helps make sure you’re not falling behind the competition and gives you an opportunity to raise the bar by brainstorming and implementing improvements to your candidate experience.
    Being data-savvy is simply knowing how to answer your biggest questions
    For recruiters, useful information is everywhere. The easiest way to benefit from a data-driven mindset is not to overthink it. Simply start asking questions about any aspect of your recruiting process, and then take measurements to uncover answers.
    The more confident you are about the data you have on talent, their affinity for your company, and your competition’s practices, the better your process will be in finding and appealing to the best candidates.
    Shannon Pritchett is Head of Community at both hireEZ and Evry1 (which she co-founded in 2021). As a talent acquisition leader, she remains passionate about connecting companies with their most valuable asset — people.
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    Developing Skills in Today’s Workforce for Tomorrow’s Data Challenges

    As businesses are put under more pressure to deliver more with fewer resources, ensuring they’re equipped with a refined and skilled data-literate workforce is crucial. An uptake in technology adoption will mean very little, if only a fraction of the workforce has the skills needed to reap the benefits. With upskilling and training integral parts of technological advancement, it’s vital to ensure that all workers are able to question and understand how to use the data and insights received from any new technology.
    PwCs CEO research has recently revealed that 61% of CEOs’ tech investment is focused on transforming (rather than just maintaining) their current business to ensure they can endure today’s economic turbulence. As they seek to gather and provide the necessary skills to remain competitive and relevant on a global scale, the skills shortage presents an ever-changing challenge for companies. While some skills gaps have closed, new shortages have emerged. This is also true for data analysis. With data everywhere and every department in every business trying to work with data, the challenge is due to a great demand for, but an equally significant shortage of, highly qualified data scientists.
    Building a workforce from within, not from scratch
    As businesses grow and the demand for data analysts increases, the options business leaders can take to differentiate themselves is simple: grow what is already present. Tackling the data skills shortage is a journey. While companies often look outwards for technical solutions, the key component to successfully developing a broader data-literate workforce comes from within.
    Delivering the decision intelligence required to deliver split-second business insights in real-time requires employees who can competently work with data. These workers don’t need advanced coding skills but instead are often the in-department experts. Knowledge workers in the line of business are an often-untapped resource. With hard-won domain expertise and the ability to effectively combine this knowledge with code-friendly and/or code-free self-service technology, they can easily address their data problems creatively. Whether businesses have data on-premises, in the cloud, or somewhere in between, the combined approach of accessible self-service data platforms and data-literate knowledge workers can make data-literate workforces a reality.
    Data literacy and technology
    It’s been proven that we are not only operating in the greatest period of data generation in history but also in a remarkably disrupted global economic landscape. While this data surge provides new opportunities for delivering decision intelligence at scale, many businesses struggle to deliver the insights they need at the required speed and scale. Research completed by Statista proves this, as it showed that just 2% of the data produced and consumed in 2020 was saved – and retained – into 2021. This presents a huge opportunity for organizations across the world to train and upskill their current workforce in data literacy so they can understand, interpret, and apply data-driven insights to decision-making.
    Data literacy and the ability to harness analytics effectively are key to delivering value from data. Data literacy is also a crucial tool when it comes to developing the next generation of data science talent. It can make a huge difference in how data is interpreted or how AI algorithms are trained. But technology alone cannot solve what is a very human problem. Technology is only ever a facilitator of the human expertise behind it. The analytics skills gap won’t be resolved by teaching more people coding or buying more technology. Instead, business leaders need to focus on democratized analytics – the enablement of anyone in an organization to work with and deliver value from data.
    Upskilling is a continuous investment
    Developing data literacy and digital skills boils down to how business leaders engage these domain experts – allowing them to lean into analytic opportunities, discover new use cases, and deliver specific end results through a continuous cycle of improvement. Any company aiming to tackle its data skills shortage must focus on the journey: utilizing, upskilling, and enabling the knowledge workers they already employ to support existing data science teams. Domain experts understand the nitty-gritty of the business but might lack the technical knowledge to make data-based decisions. Upskilling this set of employees will levitate the business growth. They already possess the skills to make impactful business decisions; reskilling them with powerful analytical knowledge will provide them with the ability to back these decisions with data insights.
    As the need for data intelligence grows, industries must look at how they fulfill their data and analytics needs. The benefits of improving data literacy are plentiful; it helps businesses earn employee loyalty. It helps employees grow their careers as individuals by learning more efficient ways to use data; it helps businesses earn employee loyalty, as well as create a data-intelligent workforce capable of efficient decision-making. In this age of unprecedented data creation, the untapped potential of a data-driven workforce without a data science qualification is yet to be seen, but the possibilities are endless. Yet the upskilling and reskilling of all employees – from IT to sales, accounting, and marketing– in data literacy needs to be a continuous investment. Businesses that understand this will lead the charge by creating a culture of data literacy where anyone can leverage data for strategic decision-making without relying on skilled data workers. Those who fail to take these steps will quickly fall behind the competition.
    By Libby Duane Adams, Cofounder and Chief Advocacy Officer at Alteryx.
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    Data and Financial Technology: What it’s Like to Work at Bloomberg

    Finance and business professionals are faced with the challenge of navigating increasingly large volumes of data to make the most informed decisions possible.Bloomberg, a financial technology company, plays an important role in connecting the world’s decision-makers to accurate information about the financial markets. Through innovative tools such as the Bloomberg Terminal, the company provides access to financial data, news, and insights that help clients turn knowledge into action.Bloomberg’s Data team is responsible for the end-to-end intake and delivery of data behind the company’s products and services. The team processes billions of pieces of financial information daily and plays an integral role in ensuring the breadth, depth, and quality of financial data for which Bloomberg is known.In this article, we’ll learn more about the Data department and its ever-evolving role at Bloomberg and within the financial technology industry. Additionally, we interviewed James Hook, the Global Head of Data, about the latest industry trends, his career trajectory, and the traits and skills needed to succeed in Data roles today.Bloomberg’s Data Department At Bloomberg, data analysts combine both financial market knowledge and technical expertise to collect, analyze, and supply the most relevant content to clients operating across industries and asset classes. At its core, the team is primarily responsible for: 1) acquiring, modeling, and enriching proprietary and third-party data so that it can be used by clients, 2) providing support to client data queries, and 3) innovating workflow efficiencies that enhance the company’s systems, products, and processes.Data management is an emerging area that comprises many academic

    disciplines, including data modeling, artificial intelligence, data science, and data quality management. Individuals who focus on data at Bloomberg are given training and client exposure to better understand how data fits into the financial markets. As the data industry evolves, ample opportunities to expand one’s skill set and experience emerge across various domains.The rapid evolution of the industry also requires Bloomberg’s data team to adapt to the latest trends and practices to help clients solve their current business problems. The team proactively stewards data that addresses future client needs.

    The Career Trajectory of James Hook

    During his 18-year tenure at Bloomberg, James Hook has embodied what it means to be an effective team member and leader. At Bloomberg, James has demonstrated an ability to work across different disciplines, first as a software engineer and later as a Team Leader of the Structured Products team, where he developed both technical and financial market expertise to deliver valuable client solutions.That experience led him to the Data Technologies Engineering team, where he  managed the talented individuals who built the systems that on-board all the reference data that drive Bloomberg’s applications and enterprise systems. Now, as the head of the Data division, he focuses on combining domain expertise with innovative data management techniques to increase the overall value of our data products

    Through his intellectual curiosity, collaborative spirit, and diligence, James has grown his career at Bloomberg. While his path is unique, it speaks to the career possibilities available in Bloomberg’s Data Division. Global Opportunities in Data

    During our interview, James Hook described what Bloomberg – and the data industry in general – looks for in candidates, and the qualities it takes to succeed and make an impact as an employee.

    Successful Bloomberg data professionals can come from a variety of academic backgrounds and share two common traits – first, an aptitude to delve into specific data and financial domains, and second, an appetite for innovation and experimentation.

    In addition to developing subject matter expertise and technical skills, data analysts collaborate with a range of stakeholders around the globe, from Engineering to Sales, on product development.While most of Bloomberg’s employees work in major cities around the world, many data analysts work in Princeton, NJ, a suburban community about an hour from New York City. The Princeton location offers a research campus-like culture and boasts open, outdoor spaces that facilitate unique collaboration opportunities among the departments working there. This location also allows the team to build relationships with local universities and foster a continuous learning environment where new and bold ideas are developed, tested, and brought to life.With a diverse data team based around the globe in Princeton, New York City, and more than half working in Latin America, Europe, and Asia, James is focused on nurturing a culture of collaboration and inclusivity throughout the department and the broader organization as a whole. 

    Bloomberg runs on data. Thus, developing strong data management skills is critical for Bloomberg’s continued and sustained growth as a company. Positioned at the intersection of technology, finance, and data management, employees have a lot of range to explore their passions and pursue different projects throughout their careers with the firm.Not only is the culture and environment of Bloomberg special, but also the company’s philanthropic mission makes a real impact on the world and is incredibly motivating.If you’re interested in learning more about open roles at Bloomberg and taking a step further into the world of data and analytics, check out their current job listings. As the world of data grows, so do the opportunities, so don’t hesitate to make your move! More

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    3 Reasons Why We’ll Continue Remote Interviewing Post Pandemic 

    Interviewing has gone remote. While some think this is temporary, it’s actually the future.
    When the pandemic began, businesses were focused on putting their heads down and weathering an uncertain economic environment. For many, this meant a temporary freeze on hiring. As things thawed, hiring came back; but this time, it was remote.
    A Gartner poll found that “86% of organizations were incorporating new virtual technology to interview candidates” by the second month of the COVID-19 pandemic. This mad scramble to integrate new interviewing tools was, for some, disorienting; but in tech hiring, it’s been a godsend. While we still appreciate face-to-face interaction, the digital nature of remote interviews comes with so many benefits that we won’t be whiteboarding coding challenges with candidates ever again. Here are three reasons why:
    1. Less Work; More Data
    I work in tech and love data. Tech companies evangelize harvesting data wherever possible, but before the pandemic, interviews were a data dark spot. Most of the information conveyed lived either in the mind of the interviewer or via their notes. Even if there were coding assessments that added a quantitative element to the interview, these were often done on whiteboards or pieces of paper that then needed to be digitally transferred. The result was that interviewers often spent an extra 30 minutes simply capturing what had already transpired.
    Today, the entire process is digital, which means that so much more data is automatically captured, and it’s now being put to use. We have transcription tools and video recordings that make reviewing the interview that much easier. According to HackerEarth’s State of Developer Recruiting 2020, 56.9% of recruiters said a major benefit of remote interviewing came from pair programming with a collaborative code editor, as this automatically captures and assesses a candidate’s coding skill in a collaborative, work-like environment. We even have automatic feedback generators that request performance input after specific questions. These are then compiled into an after-action report that simply needs to be edited rather than written from scratch.
    This means that interviewers spend less time writing and more time carefully weighing a candidate’s skill. Starting digitally puts all the data at our fingertips and allows us to make the most informed decision. Instead of a data dark spot, remote interviews are now richer than a resume.
    2. Geographic Flexibility
    There’s no question that tech has a talent shortage. Only 60% of all tech positions are filled. When we were dependent on in-person interviews, we constrained our talent pipeline even further. With tech roles only becoming more important over time, we can’t think locally about tech hiring anymore.
    The pandemic opened up new talent reserves in geographically diverse locations. We can now hire anyone from anywhere. According to HackerEarth’s State of Developer Recruiting 2020, 50.6% of recruiters say that remote interviews are beneficial due to their logistical flexibility. A further 40.4% said they saved significant time. Remote interviews with built-in features like pair-programming and real-time code editing, which now constitute 11.1% of all remote coding interviews conducted, have basically solved the problem of onboarding the most qualified candidates regardless of location.
    There is now a bigger pool of tech talent that can work from anywhere, and assessing them remotely has never been easier. In fact, 30.7% of recruiters said that remote hiring had actually increased their talent funnel. As the global workforce becomes even more accustomed to remote work, this means that remote interviews will be a feature of the hiring process for years to come.
    3. Reduced Bias
    57.6% of enterprises have placed extra emphasis on hiring for diversity in 2020. But as much as we love meeting candidates face-to-face, first impressions are often clouded by personal biases that can unintentionally limit diversity. Recruiters and hiring managers tend to prefer candidates that mirror their own backgrounds in what has been termed by researchers “Looking Glass Merit.” While interpersonal and other soft skills are absolutely important, face-to-face interviews sometimes overvalue them relative to hard skills.
    Thankfully, remote interviews add a layer of separation that gives interviewers input on things like body language without placing undue influence on them. While 10.2% of recruiters at SMEs say that challenging unconscious bias is still a major pain point, and 13% of recruiters are specifically choosing assessment tools that help eliminate bias in the interviewing process.
    One way to combat this problem is to mask personally identifiable information (PII) during remote interviews so a candidate’s skills can speak for themselves. This means things like their name, gender, academic background, etc. are hidden during the interview itself, so the interviewer’s impression of a candidate is solely based on their skills.
    A Remote Interviewing Future
    Even after a vaccine is widely available and things start to return to ‘normal’, we won’t be looking back at how we used to hire. We may still meet candidates for in-person interviews from time to time, but will certainly continue to use digital interviewing tools for a better interviewing experience. Pair programming is just better on a computer, and we shouldn’t want to go back to the days of whiteboards and hand-written notes.
    Today, tech hiring is more competitive and geographically untethered than ever, so we need to make the interviewing process as convenient and flexible for candidates as possible. In the end, remote interviewing saves the company and the candidate time, and more importantly, allows interviewers to limit bias significantly relative to in-person interviews. These more objective interviews are helping managers create the best tech teams where only skills matter.
    By Sachin Gupta, CEO of HackerEarth.

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    Understanding the impact of COVID-19 on your job search

    As we move into the summer months of 2020, by now we are well acquainted with the recent events hitting markets hard and disrupting hiring patterns. Whenever we are faced with challenging times, we are presented with unique opportunities. As a job seeker, you will have to remain proactive in your search for employment. Staying […] More