Whether you're looking for an AI-powered virtual assistant, Business process outsourcing, or chatbots to help with your daily tasks, it's possible that a VA could be the solution to your company's needs. While AI-powered virtual assistants and Chatbots are great, they won't replace human skills, so the future of virtual staffing lies in the human and business interaction between them.
Artificial intelligence-powered virtual assistants (IVAs) can help companies with a variety of tasks. They can improve customer support, reduce the number of calls to human agents, and process repetitive tasks so that employees can focus on more complex tasks. AI assistants can also collect and process data - traditionally, customer support data requires an analyst to sift through hours of data to find the relevant details.
Voice-assistants can break down social barriers by enabling employees to engage with customers via a conversation rather than relying on typing. Many GM Financial employees reported increased job satisfaction with the new solution, which reduced their workload and required fewer staff. With a growing number of companies conducting business online, voice-assisted customer service is the solution of choice. In fact, 79% of contact center leaders plan to invest in greater AI capabilities over the next two years.
The practice of outsourcing company functions is known as business process outsourcing. Businesses will hire a third party to perform the tasks that do not require their core competencies. The third party can be based in the same country or a different one. Examples of non-core tasks that a virtual assistant (VA) can perform include data entry, customer service, research, graphic design, software development, and personal services. This method of outsourcing can be a highly efficient way to hire additional staff for a business.
Businesses of any size can benefit from the services provided by virtual staffing. A small restaurant, for example, has employees in the kitchen, the waiting area, and preparing drinks. These functions can be broken down into specialized tasks that require specialized skills. Virtual staffing can help this restaurant save money by bringing in qualified individuals who can perform these tasks more efficiently. The same is true for companies with large amounts of information to analyze.
AI-based chatbots can help you out with customer service. This type of technology is based on intelligent personal assistants such as Siri and Google Home. These artificial intelligence systems can understand nuances of human language, and are capable of making appointments and understanding moods. They are far more sophisticated than chatbots, which are based on architecture and models that are designed to respond to questions. Moreover, chatbots can be operated without a dedicated team.
A good chatbot should be able to use stored data to automate mundane tasks and maintain situational awareness. Additionally, bot conversations should be short and easy to understand. To achieve this, chatbots should not use graphics, and should be able to store conversation histories for further reference. If possible, users should be able to program the chatbot to respond to a question or request in multiple texts. However, bots should also be capable of storing conversations to improve their efficiency.
The cost of hiring a virtual assistant varies depending on the skillset and experience level of the individual you hire. Virtual assistants can perform a variety of administrative duties, including fielding calls after hours, generating traffic for your online storefront, and handling financial projects. Some VAs specialize in specific areas, such as invoice processing, vendor relations, and receipt transcription. These virtual assistants can also help you with general administrative tasks, such as scheduling travel and type notes and documents.
Project-based packages are often more expensive than hourly rates, as VAs may have specific skills that you don't need. If you're planning a one-off project, a VA can input contacts into your email marketing platform, craft email copy, monitor bounce-backs, and more. However, if you have a large budget, a project-based package is worth considering. You'll pay at least half of the total cost up front and the rest when the project is complete.
#virtualassistant #staffing #recruitment #recruiting #recruiter
As per a new report, over 70% of U.S. laborers are thinking about or effectively searching for a new position.
Losing a top worker is each director's bad dream, particularly in a tight work market. Putting forth attempts to further develop representative maintenance should be high on each organization's need list.
So what makes a decent worker leave, and would it be able to be forestalled?
As an administrator, it's not difficult to work your best individuals the hardest. All things considered, they set forth the most energy and make you look great. Then again, the worker can feel like they are being rebuffed for their usefulness.
Be resolute about work/life balance. While it's occasionally important to answer an email or call outside of the workplace, it ought not be a propensity. We as a whole need time to re-energize. Assist your group with making an unmistakable partition among work and home. They will see that you esteem their opportunity and will return to work every day revived and all set.
(Remember that you show others how its done. Assuming you're routinely working almost to excess or shooting off messages night-time, your representatives will see that as the assumption. Deal with yourself, as well.)
Indeed, even the coolest organization advantages start to lose their allure when representatives don't feel like they are developing as an expert. Top representatives need to be enlivened and afterward get the valuable chance to follow up on that motivation. It's the supervisor's liability to keep recognizing regions in which their immediate reports can grow their range of abilities. Great administrators make due, regardless of how skilled their immediate report might be. They focus and are continuously giving criticism.
Consider executing an inside recruiting process in the event that you don't as of now have one. Show an inclination for hiring inside. This permits confided in specialists inside your association to propel their professions and progress up the stepping stool. Recruiting inside likewise definitely brings down the expense of an all out employing search.
Keeping great representatives requires steady consideration and care, similar as maintaining a business. Be mindful of the requirements of your representatives to make shared trust and regard. It is generally difficult, however the prizes are really great for business.
Absence of correspondence, under-correspondence, miscommunication… Whatever you need to call it, it's a far and wide issue. It breeds vulnerability and sabotages the certainty of good representatives. They start to re-think themselves and feel that they can't confide in one another - or their chiefs besides.
Significant correspondence isn't giving an explanation. It is a discourse intended to arrive at shared belief for the two sides. Instead of passing down in a mandate, urge your immediate reports to partake in the dynamic interaction whenever the situation allows. Be available to groundbreaking thoughts. Advance fair conversations inside your Company.
Great representatives are an interest in your organization's future, yet you don't keep them coincidentally. Deal with individuals that deal with you and watch your most significant organization venture pay off.
#company #staffing #recruitment #recruiting #recruiter #hire #hiring #usa
During my recent job hunt I realised that there are lots of blogs out there highlighting the differences between Data skillsets (Analyst, Data Scientist, Machine Learning Researcher etc). However, I didn’t come across many that explore how these skillsets tie in with different business functions of a company (Marketing, Product, R&D etc).
Many of the roles I came across had the same titles but the requirements and responsibilities of the roles were completely different. Making this connection between data skillset and business function not only helped me classify the different roles I discovered but also helped me understand how I wanted my career to progress. I created a framework to help with this and I wanted to share it with other budding Data Professionals that want to understand the differences between the skillsets required and the business problems that each role would focus on.
The other thing that I experienced was that there is sometimes a gap in understanding of the role between recruiters/hiring managers and applicants. This gap often isn’t highlighted until the 2nd or 3rd stage of the interview process. So I guess this post could also help hiring managers and business owners better understand what kind of Data Professional they are seeking, and in turn be able to convey that in their Role Spec, resulting in higher quality applicants and less time wasted for everyone.
#data #big-data-career #recruitment #data-science
The rising interest in applicant tracking systems should not come as a surprise. Our research has shown that the majority of modern-day companies do not, in fact, use ATS solutions for their recruitment needs. But this rise in interest has changed the landscape in recent years.
Recent developments seem to focus on utilizing disruptive technologies when these solutions are created, artificial intelligence chief among them. ATS solutions are becoming more popular, and rightly so. These SaaS products are capable of turning basic hiring processes into a well-defined strategy, one that requires very little effort to put in place.
A good hiring platform can help recruiters find, vet, and assess incoming talent much more effectively than keyword-focused research and LinkedIn browsing.
Beyond basic talent sourcing, AI-powered ATS solutions are able to rate and assess candidates, match them to the most relevant position and provide scoring insights into their skill, experience, cultural fit, and how likely they are to perform well in certain situations. It’s the modern way for companies to identify the most relevant and valuable talent at their disposal.
#artificial-intelligence #hrtech #ai #recruitment #recruiting-software #ats #applicant-tracking-system #good-company
Finding the right talent in data analytics, data science, artificial intelligence, and other related fields is critical to a company’s overall health.
And if the company wants to outsource hiring analytics talent, it is important to choose the ideal recruitment agency. There are various factors to consider — from specification to the budget — for picking the right agency.
Read more: https://analyticsindiamag.com/how-to-choose-an-analytics-recruitment-agency/
#analytics #data-science #artificial-intelligence #recruitment
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#php #hire #recruitment #job
I hope you will find this post helpful, whether you’re a recruiter, or if you’re trying to ascertain your own position within the DevOps definition, or if you need to have that ‘DevOps guy’ in the team, then keep reading on;
Let’s see what the fuss is all about. Thoughts on experiences are my own.
It comes as no surprise that Hiring for DevOps is hard.
In the ever-evolving world of technology, — it has a profound meaning for some, while it means nothing for others.
It is indeed probably a bit of Both.
There are many particular Physical and Virtual, hybrid infrastructure architectures, OS-family-based infrastructures, then there is a myriad of build release pipeline implementation options. Then there is a number of configuration automation frameworks, and now there is a new kid on the block. The Cloud, with own set of product offerings particulars and caveats, with underlying technology stacks, namely Docker, and more recently Kubernetes product offerings. This makes VMs look like the DVDs look too young generation of 2019, — “It works, it’s great, but why do I need these accessories, when I can just stream”.
When it comes to looking for “DevOps guy” it can basically mean… “anyone who has worked with, on, any-of-the-above particular technologies”, — the IT version of “Jack-of-all-Trades”.
So who are we after?
#roles #recruitment #jobs #meaning-of-devops #devops
I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Please give it a try by subscribing below:
LinkedIn is one of the favorite recruiting platforms in the market. Everyday, recruiters from all over the world rely on LinkedIn to source and filter candidates for specific career opportunities. Specifically, LinkedIn Recruiter is the product that helps recruiters build and manage a talent pool that optimizes the chances of a successful hire. The effectiveness of LinkedIn Recruiter is powered by an incredibly sophisticated series of search and recommendation algorithms that leverage state of the art machine learning architectures with the pragmatism of real world systems.
It’s not a secret that LinkedIn has been one of the software giants that has been pushing the boundaries of machine learning research and development. In addition to nurturing one of the richest datasets in the world, LinkedIn has been constantly experimenting with cutting edge machine learning techniques in order to make artificial intelligence(AI) a first class citizen of the LinkedIn experience. The recommendation experience in their Recruiter product required all LinkedIn’s machine learning expertise as it turned out to be a very unique challenge. In addition to dealing with an incredibly large and constantly growing dataset, LinkedIn Recruiter needs to handle arbitrarily complex queries and filters and deliver results that are relevant to a specific criteria. Search environments are so dynamic that result really hard to model as machine learning problems. In the case of Recruiter, LinkedIn used a three-factor criterial to frame the objectives of the search and recommendation model.
1) Relevance: The search results need to not only return relevant candidates but to surface candidates that could be interested on the target position.
2) Query Intelligence: Search results should not only return candidates that match a specific criteria but also similar criteria’s. For instance a search for machine learning should return candidates that list data science in their skillsets.
3) Personalization: Very often, finding the ideal candidates for a company is based on matching attributes that fall outside the search criteria. Other times, recruiters are not certain of what criteria to use. Personalizing search results is a key element of any successful search and recommendation experience.
#overviews #linkedin #machine learning #recruitment
Bias in the recruitment process is increasing with most companies scoring poorly according to a 2019 Gartner poll. The Amazon incident where AI systems biased¹ against women shows major recruitment problems among companies today.
Artificial intelligence works best with human augmentation and addressing diversity bias is a step towards making AI systems² work better.
Salesforce¹⁰ is addressing bias through their inclusive culture, which attracts diverse talent and tech companies should follow this example.
#bias #recruitment #artificial-intelligence #careers #technology #ai
Starting a company is a learning process, you have to challenge your own beliefs regularly and not be afraid to try new things. For a young company, hiring is critical, the wrong developers can cost not only money but months of time.
One decision I made quite early on was that I didn’t like code tests. As a developer, I had previously had a few poor experiences during interviews for developer jobs including people not explaining the problem they wanted me to solve, leaving out key details or challenges that are easy if you learned the trick (hint: they aren’t a real test). I thought they could be patronising and demoralising.
Though our CTO is an advocate of code testing, we agreed that on-the-spot code tests during an interview were not a natural way to code. So we did not do them, even though that fails the Joel Test: https://www.joelonsoftware.com/2000/08/09/the-joel-test-12-steps-to-better-code/
This month has totally changed my perception of that, we’re in the process of hiring more developers at the moment but we had a time crunch. Generally, we follow the same process with all hires, initial call with the CTO, wider interview with three of us, meet the team and if everything works out then we’ll make an offer.
The challenge this time around is that we had four open roles, two frontend and two backend but on the frontend side neither our CTO nor I really knew the technology in depth. We had a time crunch, so our Agile Coach suggested that we try implementing a code test, his rationale is that it acts as an initial filter for candidates so we can focus our time on the best candidates and streamline the process.
We agreed on some key attributes of this code screening test:
The results were nothing short of stunning and disappointing at the same time. More than 50% of the candidates we put through the test could not complete it in the time given, there was a direct inverse correlation between salary expectations and code results (more expensive candidates did worse!) and over 60% of the candidates did not meet the minimum standard.
#jobs #recruitment #hiring #startup #software-development
Less than a decade ago, we could only dream of being able to spend five minutes on a job that required 10 hours to complete. All we could hope for was a magic wand. Cargo dancing with tambourines could also help, but there was no guarantee (pun intended).
Today, as technology becomes more advanced, we’re gradually getting used to a “new normal” when most things are done instantaneously thanks to automation, Cloud, artificial intelligence (AI), machine learning (ML), and advanced analytics. Physical boundaries are fading away and what once required physical presence can now be done remotely, faster and much more efficiently.
Powered with automation and big data, any technology becomes cutting-edge and sophisticated. Recruitment tech isn’t an exception.
From accelerating employee selection processes and managing the pipeline to activating a dormant talent pool and reducing administrative burden – recruitment tech enables consultants to make more informed decisions and proactive process interventions to improve the overall quality of hiring.
As most hard skills are expected to be automated in the years to come, both corporate and agency recruiters should be ready to transform into tech-enabled strategists, and act as middlemen between stakeholders and digital platforms that begin to play a crucial role in recruitment evolution and digital transformation.
To understand better how recruitment innovation helps simplify hiring and reinforce informed decision-making, I’ve zoomed in with Jamie Beaumont, an entrepreneur and founder of Offerd, a UK-based recruitment platform that enables 100% remote candidate screening, interviewing, hiring, onboarding and more.
Jamie and his team were able to launch their product to market during the COVID-19 pandemic, which makes their case even more thought-proviking.
We’ve talked about key challenges facing corporate recruiters and hiring agencies alike, why AI is not a silver bullet, what it takes to launch a recruitment tech product amid the Coronavirus pandemic, how to use big data and insights to improve the quality of hiring and more.
Enjoy the reading!
Having graduated from the University of Exeter, I wondered what I was going to do next, and I ended up going to the mountains for skiing. There I noticed a big fashion trend among people on holidays: many were wearing old-school clothing like the bright red gear. As the retro look regained its position at the top of the fashion charts, I just couldn’t stay on the sidelines and co-founded a fashion brand with an idea of bringing a retro style back to the mountains. The business went well, but after almost two years, I came back to the UK, where I joined a graduate recruitment company as a consultant. Although I managed to rise through the ranks, I left it about a year later to start my own recruitment company – it was a graduate brand within another company called Sales Point. We did really well and managed to drive a significant amount of revenue.
And then the opportunity came to actually go back and purchase the company I started out with. My business partner and I acquired that recruitment business, and we merged two companies into one. As the company scaled, we opened another office up in Manchester and hired lots of people.
During that time, I spoke to many businesses and they all complained that hiring through an agency was expensive, vetting candidates on their own was time-consuming, and there was no way to do things remotely.
_As I collected those insights, I said to myself: “There should be a way to solve all of those problems!” And that’s where I saw a new opportunity for myself. _
So I left that recruitment business in February 2019, and put my entire life savings and more into starting my own recruitment technology business and building the first piece of software. I raised £300,000 with three fantastic investors who are still with us and who have been unbelievably amazing and supportive along the way. They’ve re-invested earlier this year which allowed us to build a decent product, launch it to the market during the Covid-19 pandemic and onboard customers despite the crisis.
It’s been a great journey from selling clothes in the mountains to owning a technology business, and we’ve got bigger things on the horizon as well. As we raise more funds, we’ll hopefully start a new product which is a game-changing financial offering for recruitment agencies and hiring businesses alike. It’s in stealth mode now so I can’t reveal much about it.
Many universities struggle to secure employment for their students after graduation. At the times of mass unemployment, with experienced individuals being unemployed, students don’t stand a chance. When you need to choose between hiring an experienced and an inexperienced person, most will choose the first. The idea was to help universities make students more employable, and we started thinking about how to increase the employment chances of each graduate coming out of the university. However, we’ve put that on ice for the moment and will “package” it next year when the academic world gets back on track.
In fact, 20% of our customers are direct hiring businesses, i.e. all types of companies that hire individuals themselves. We cater to smaller businesses that don’t want to pay for complex enterprise software. As much as 80% of our client base – what we really want to specialize in – are small and startup recruitment agencies whom we give access to really cool remote hiring features. Instead of spending thousands of pounds a month on enterprise software, we offer them a great solution to get to the market and hire. At the moment we see a good increase in customers like this.
This statement is based on the insight we’ve gained from a couple of the actual clients using our platform.
A traditional structure of a recruitment business is as follows: you post a job, get a CV, read that CV and look through it to make sure it’s a good match. You may arrange a phone call and ask a few questions to make sure they’re relevant to the role and have the right qualifications, and then you invite them to a face-to-face meeting or a virtual one in Skype or Zoom. All of that is taking 1-2 hours to complete.
With us, recruiters get all of that done for them: when candidates apply for the role, they complete a screening questionnaire, upload their CV, have a small profile and hold a video or voice interview. So instead of two hours, they literally spend sixty seconds and save a good amount of time.
Our product helps them triple the capacity of their recruitment consultants and improve the quality of hiring, which helps increase their revenue threefold.
#recruitment #recruiment-software #founder-interview #founder-stories #founder-advice #entrepreneurship-experiences #startup-advice #startup-lessons
I’ve recently lost the contract. It was unexpected, and a few long minutes of shock followed the Slack call during which I’ve been informed about the situation. It felt like hours when we just sat down with my wife and kept silent trying to figure out how much savings we have and how difficult it’ll be to secure a new position in the current mid-COVID world.
There was no time to lose, so after making the coffee ( this time a bit stronger than usual ) I’ve headed towards the LinkedIn to send the bat-signal out, found my CV hidden somewhere on one of the devices and started browsing the “jobs” category.
It’s been a while since I’ve touched it, and last time I added my current role to keep it up to date as possible. My wife put her “very serious but loving face” on when I told her my CV was quite a few pages. It was a sign of disadvantage of highly technical jobs from your past vs recruitment or hiring managers who quite often are not involved in the technology world but look for the specific buzzwords, keywords or technologies matching. “Shorten it up, leave last few roles with things you’re proud of, remove description bits and compress everything else into the smaller, quick to browse through chunk” — she said. The WIFE is always right, so who am I to argue? After half an hour of cutting, trimming, extracting keywords just to make sure I’ll be “discoverable” by the recruiters, the summary of my past 20 years of experience was ready to be shared with the world.
Following the “no time to waste” — I’ve sent the new version of my CV out to the recruiters I completely trust for the proofreading and bug hunting. With their input, I was able to tailor it even better to the current market requirements. I’ve lost my contract on Wednesday around 2 PM. By 4 PM I’ve managed to upload the newest version of my CV and profile to the top 15 job search websites in the United Kingdom, and I’ve spent next half an hour on searching and applying for the jobs posted within last week on LinkedIn — just to make sure. I’ve had first few calls and messages the same evening, which together with the well-wishing community of the LinkedIn boosted my mood and left me with a compelling need to push for more.
My wife made the whole job search a really amazing experience. She supported me on all possible levels, high fived every recruitment call and cheered every interview I’ve had whilst working remotely her daily job from our “office” room. I can’t even express how much happiness, joy and energy her support gave me from the beginning until the end. She never allowed me to hang my head, always smiled and been the best advisor. I can only summarise it with — trust your loved ones, believe the others.
There are few tricks which nobody mentions, maybe not too many people are aware of, and recruiters do not disclose that quickly, and I’ve learned about in the past few years.
Most of the recruitment companies and software operates on keywords — think about your CV as a document which you want to be easy to find in Google. Throwing in too many or unpopular keywords won’t make sense, will make it less engaging and challenging to read as well. It can also send the wrong type of job offers your way. If you, for example, have five years of experience with Ruby and Python, but you’re a DevOps engineer who doesn’t want to be a developer (trust me, you’ll get a lot of calls for the dev roles ) — avoid specifying those in your resume, add “programming in multiple languages”. On the other hand — if you worked with Cloud technologies — quite often you need to list them one by one to make yourself easy to find.
Talking of the presence and software used by recruitment companies — you will need to wake up early on Mondays and Wednesdays with all the job offers websites links ready to **re-upload your CV, **as that’s the time when their software imports CVs from the websites business subscribes. I’d recommend the start of this process around 7 AM to make sure your resume lands in the recruitment companies databases as one of the first ones.
Start posting regularly on LinkedIn and make sure your posts are visible to the public. Loss of job isn’t anything to be happy with, that’s for sure, but posting about your progress in the search, any new things you’re doing to improve the process and maybe side projects you work on — will increase your visibility and encourage others (even from outside of your network) to interact or help you out. Use appropriate #hashtags within your post (in moderation, don’t overdo it) to increase the size of the audience and don’t forget to engage with other posts.
The last thing, really often ignored — make sure your picture in social media, job search websites and so on is recent, bright and you are the only visible subject. Avoid photos from holidays, masks on or “cool parties” — you can leave it for your Facebook and Instagram friends, and we are here to find the new role which will allow you to thrive. Pick a picture which shows you the way you are — honest and trustworthy person who’s capable of bringing experience into the business.
Make it your goal to answer every call, reply to every message and e-mail. Leave your social media behind, you’re on the hunt, and you must avoid any kind of distractions. Yes it’s quiet but forget about the “few minutes of Netflix”, you need to be ready to pick up the phone when it rings (keep it on loud).
Do you remember when the last time you called your best friend, family member or spouse was? You most likely were able to pick up the vibe or feelings of the other side. Recruitment specialists spend most of their time on the phone. They are in most of the cases extremely friendly and easy-going people (those who are not, usually leave the business quite quickly), so if you’re an introvert like me — I am sorry, but you must smile when you answer the phone. Every time you have a call to answer — think about your dream job, best moments with your spouse or friends — doesn’t matter — do everything to take every call as a chance to find yourself in that happy place over and over again. This will set your mind for success by default.
#recruitment #programming #careers #interview #job-search
Over the past years, I have conducted numerous interviews either as hiring manager or as interviewer supporting other hiring managers in their decision-making. In almost all instances, the interviewers of a candidate exchanged their interview evaluations in written form before we met in person for a debrief. For my interview evaluations, I have developed a lightweight structure that I strictly follow for every non-technical interview that I conduct.
In this article, I’m going to share this structure with you and explain my motivation behind it. It might seem trivial to you and if I’m honest with myself, it probably is. “Lightweight” just makes for a more appealing headline, I suppose 😏. Be that as it may, this structure has served me quite well on many occasions and I’ll explain why throughout the article.
Following a strict structure when I write my evaluations allows me to gather and organize my thoughts while I go through my notes from the interview. The structure does more than help to externalize my impressions from an interview into a condensed, digestible form, though.
It’s my personal safety check to verify that my evaluation is conclusive: Do the key insights I took from the interview _really _support my recommendation (hire/no hire)? Are they exhaustive? Do they paint a fair picture of the candidate?
Coming out of an interview, I often already leaned towards either hire or no-hire. Forcing myself to go through the structure nevertheless helped me to solidify that first hunch – or to refute it! It happened more often than I care to admit that my immediate impression was mostly backed by just one or two particularly memorable exchanges from the interview. When I sat down again to write my evaluation, the flaws of my initial assessment became apparent quickly. I also had cases where I felt really unsure about a candidate after an interview only to realize there was a solid case hidden in my still unstructured notes.
Interviews are an attempt to evaluate a candidate‘s capabilities and character traits to assess their fitness for the job position in question. That said, it would be presumptuous to believe that an hour-long interview suffices to evaluate any person with 100% accuracy.
That’s why I make a deliberate effort that the language I use in my interview evaluations reflects this uncertainty. At the cost of brevity, I write “I had the impression that…”, “This indicated to me that…”, “This led me to believe that…”, “This felt to me as if…”, and similar.
Every interview evaluation starts as a scaffold of sorts. I write down a series of headlines with empty bodies, which I fill with life while I go through my notes. These are the six sections:
I’ll explain what goes into each section below.
For the convenience of the reader, I put this section first. But I actually write it last! I don’t give in to the temptation to share a recommendation prematurely only so that I can quickly move on to the next task on my to-do list.
I fill the other sections as I walk through my notes. I make another sweep through the notes to make sure I haven’t forgotten anything of relevance. Only then, I carefully read what I’ve written and finally compile my conclusion.
The conclusion is a statement of two to three sentences that aim to succinctly explain my recommendation to either hire or reject the candidate. The recommendation can be one of four distinct values: strong yes, soft yes, soft no, strong no. Nothing in-between.
Once I’ve written my conclusion, I run a small exercise: Reading the other sections again, I ask myself, can one possibly make the case for the opposite recommendation? If it’s possible, I need to revisit my notes. In any case, this is a clear sign that my recommendation can only be a soft yes or soft no.
Again, language matters. I intentionally named this section I like, because despite employing standardized interview questions and rubrics, there’s no denying that any evaluation is ultimately subjective. Every interviewer has their own preferences, their own experiences that shape what they expect from a candidate in that job position, and their own subconscious biases.
In this section, I list insights that I gained throughout the interview which support the hiring of the candidate. I list them in order of relevance to and impact on the job position in question.
#recruitment #interviewing #hiring #engineering-management #interview
Interview is not alien to any of us. By Definition, An Interview can be described as,
An interview is a formal meeting at which someone is asked questions in order to find out if they are suitable for a job or a course of study.
An Interview involves two primary Persona.
There are plenty of reads on how to get ready for an Interview as an Interviewee. However the need is from both the ends. An Interviewer has lots of stake in making an Interview a Great one or, a Horrible experience for some one.
Here are few points/tips that the Interviewer must keep in account to make an Interview effective.
Yes True, you as an Interviewer needs to prepare for the interview. You got to know about the candidate you are going to meet. Read the profile/resume/CV of the candidate. Sketch out of your understanding of his/her Technology Strength, Probing points and ambiguities from the Profile.
If you haven’t got a Profile in advance, raise a flag to your Manager or HR that, you may not be able to do your job properly in that case.
You as an Interviewer will be meeting a person (mostly) who is unknown to you, your nature, your habit. The best way to put your first impression right would be, Greet the person well with a firm handshake and introduce yourself.
You can do a small/casual talk to keep the nerve down for the Candidate and then get into the usual business of Interview. However don’t overdo it! Having a proper greeting/welcome set a very good tone for the entire Interview.
Believe or not, Interviewing is a Skill to master. Unless it is learned well, you are going to waste,
You need to learn the skill of judging where to stop, when to decide that, it is done, what to do when it is not going anywhere, how to take it to the next level. These are pure Skills to Learn.
Remember the Interview is very very less about You. You must introduce yourself in the context of your work, organization, position etc. But, please do not oversell. It is really not required as it would be a waste of time. Also, you may be setting a bad impression about yourself as a ‘Boasting Master’ to the Candidate.
Let the Interview be about the Candidate. His/Her Strength, Weakness, Opportunity, Learning, Experience etc. that are related to the work and the open position.
How do you feel, when you are telling something important to some one and that person is not listening to you? May be he is just playing a game, replying to email etc? It hurts, right?
I have seen many interviewers doing exactly the same. They keep their Laptop/Gadget open in front of them while doing the Interview. It is so very distracting for you as an Interviewer and annoying for the Interviewee. So, please shut it off.
Do not have the assumption that, the Interviewee(Candidate) will know everything. Having such assumption or expectation is going to kill the entire Interview outcome as positive.
Have your own benchmark. Keep some flexibility around it. Judge the Interviewee on various aspects, not only one or two specific items. Have a short meeting with your peers after the interview and discuss the strengths and weaknesses. Don’t take a bias call just because he/she couldn’t answer that particular question. He/She will not know everything, You too!
#interview #general advice #recruitment #general programming #tips
Data Science roles have unrealistic expectations from blog posts and other job descriptions. If you are going to copy one I hope you pick this one!
You are probably not an expert in Data Science. That’s okay, the fact that you are reading this puts you above other companies in the search for a Data Scientist.
**What is a Data Scientist?**Data Science is an ocean as deep as it is wide. A Data Scientist is somebody who can:
That list is in my priority order. Machine learning is the least important aspect of Data Science in industry, though it’s still fundamental.The above is a big ask. The title Data Scientist currently represents the jobs :
Common MistakesHere I will go over some of the common red flags that show me you don’t know why you are hiring a Data Scientist.
PhD onlyThe industry is currently divided between Data Science as a product, and as research. A few rare companies join these teams.A team focused on research and creating new novel solutions to problems has the need for PhD holders.According to Indeed, 20% of Data Scientists have a PhD, of those a minority have a PhD relevant to the field. It is not the PhD that makes these data scientists useful but their scientific approach. By requiring a PhD you are saying no to 80% of the Data Scientists that currently have experience. Scientific reasoning has a place in Data Science. I would choose industry experience over a PhD every time.
Focus on Machine LearningMachine Learning is foundational to Data Science. Without good communication Machine Learning never delivers value. Without the ability to deploy a model to production you are creating work for a DevOps team.Unless Data Science is the core product of your company you are going to find value applying third party products. Google, Amazon, and Facebook all offer tooling that requires skill to install. There is no need to reinvent the wheel. Machine Learning ability is useful for answering questions about a model’s validity.
Missing Software EngineeringSome companies will not allow Data Science to be part of the tech arm of their business. Data Science is a technology role. Good Software Engineering practices will make your Data Scientist’s products a pleasure to integrate.
Low SalaryIn Data Science both the company and candidate have high expectations. Machine Learning is a popular dream industry. We aren’t hiring Machine Learning specialists, we are hiring Data Scientists.As described above, a Data Scientist is somebody who is doing many jobs. My graduate Data Science salary was £25,000, the same pay any graduate software engineer got. If you want somebody as multi-faceted as a Data Scientist you need to put your money where your mouth is. Even more so if you are expecting PhD holders.For your first hire, you need a top Data Scientist to build up the business function. For this type of person, you are looking at around £60,000 in Manchester. This Data Scientist will have the role of Business Transformation added to the pile. Their job is to change perspectives on how the business can use data.What I often recommend is to hire either one senior or two juniors that fill in the gaps in each other’s skillset.
#data-science #hiring #data #recruitment #towards-data-science #data analysis