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With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

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How To Write The Perfect Tech Job Description

It’s a challenge finding the right Data & Analytics candidate. Add in the number of companies fighting for that perfect profile and for many it can seem like an uphill battle. But there’s a simple way to cut through the noise; better job descriptions.  As a specialist recruitment agency within the Data & Analytics space, we have seen a real variety of job descriptions over the years, from the bright and innovative to the long and technical. And it may surprise you to learn that candidates still ask regularly to see official job descriptions and it is part of their decision-making process.  Unfortunately, they are also often a part of the recruitment process that can be rushed or created from out-of-date previous descriptions. There are some real benefits, however, to putting the time and effort required into creating something fresh.   If you’ve recruited a role like a Data Scientist before, you know that the problem isn’t usually getting enough candidates through the door, it’s about getting the right ones. A well-crafted job description leads to better quality applicants. It also helps those candidates become more engaged and excited about your business.  So, with that in mind, here are our five top tips for businesses looking to help their role stand out from the crowd.  CHECK YOUR JOB TITLE  You might think that calling your BI Analyst a ‘Data Ninja’ is going to get you the top talent, but it would probably mostly cause confusion. It is important that you align the job title to a clear and market relevant job title. Often internal job titles can be the biggest blocker in aligning your vacancy to the market.  Consider changing the job title for external purposes to make it more closely aligned to the market. Here are some common examples:  An AVP Analyst within a Marketing Analytics team is more closely aligned to a Senior Marketing Analyst. A Data Scientist job title aligned to a role with no machine Learning or algorithmic development may be better titled a Statistical Analyst.  CREATE A COMPELLING JOB RUN-THROUGH  Our consultants agreed unanimously that one of the weakest areas of job descriptions tends to be the more detailed description of what the role actually is. Too often job descriptions just list lots of different responsibilities, but these are often very generic or basic.  Before starting to write the job brief, ask members of your team that do the role already – what gets them excited?  You will likely find that it has to do more with the types of projects i.e. the application of technical elements, that appeals most to candidates. If you can, bring the role to life in a meaningful way. For example, relating it to projects that your team has done is a really enticing method of exciting a candidate about the potential of the role. Create A Tailored Experience Section Uninspiring job descriptions often have long lists of key skills required, often with irrelevant skills included. Keep your requirements to around 5 or 6 key bullet points, asking yourself what the most important requirements are and clearly laying those out.  On top of that often companies get too focused on requesting years of experience. We strongly discourage companies from specifying years of experience in a job advert as, within the UK, most European countries and a number of US states this is classified as age discrimination. Instead of including years of experience, carve out what it is that you want your ideal candidate to have done before instead, this will often correlate to their experience level. For example: 5+ years' experience in a Marketing Analytics could easily be transformed to Proven commercial experience in a Marketing Analytics environment with exposure to pre and post campaign analysis, customer analysis,  customer segmentation and predictive modelling.  DON’T FORGET TO SELL YOURSELVES Another key area where many companies fall down is effectively selling their opportunity and company to the prospective candidates. Whether an active or passive job-seeker, candidates are likely deciding whether this is the right fit for them based on what they are reading. Many job descriptions completely forgo any type of sales pitch above an initial description of what the company does, perhaps because they expect the candidate to know them and want them.  These are the areas we’d suggest bringing to life to effectively sell your opportunity: Writing in your brands personality. Consider the right tone of voice to match your company culture and style of working. Introduce yourself. Whether you’re a brand name or not, use this chance to actually tell people about what you really do and what you really stand for. Share what it’s like to work for the company. Include the culture, work environment, targets, challenges and of course reference to perks and benefits on offer too. Consider the candidate. What appeals to a talented Data Scientist will differ from what appeals to an HR professional. Make sure you tailor your overall pitch to the type of candidate you are seeking.  WORK ON THE LOOK AND FEEL  A little effort on the aesthetic look of your job description an go a long way.  On top of a nice overall look, keep the length to a maximum of 1.5 pages. Utilise bullet points and bold formatting to keep the description some-what ‘skimmable’.  If you’re looking to hire a Data & Analytics professional, Harnham can help. Get in touch with one of our expert consultants to find out more. 

Sam Maughan

Sam Maughan

14. May 2020

WE HAVE TO TEACH SPECIALISATION, WE CAN’T EXPECT IT: A Q&A WITH VIN VASHISHTA

We recently spoke to Vin Vashishta, a consulting Data Scientist and Strategist who was named one of LinkedIn’s Top Voices in Data Science.  Having started off in the tech world 25 years ago and progressing from web design and hardware installation to Business Intelligence Analytics, Vin found for many years that enterprises were reluctant to adopt AI technologies and embrace the value of Data. In fact, it wasn’t until the beginning of the decade just passed that companies started to think about their Data more strategically and the world of Data Science was born, albeit hesitantly:  “When I first started, it was a lot of experimentation, everyone wanted a proof of concept,” he says. “A lot of work was creating models that could go from whiteboard to production and productise and show their value.” However, it wasn’t until halfway through the decade that he began to see businesses who had adopted Machine Learning move away from experimentation into incorporating it more deeply into their companies, relying more on analytical and optimisation models to make strategic business decisions.  “After that, in about 2017/2018 the maturity changed. It went from being a one off implementation to it being a comprehensive tool within an organisation where we have full lifecycles of model implementation and full models that were full views of the system. The key component of development was allowing users to access a small part of the system to do their job better without having to understand the whole thing. And that’s where we are now. We have this applied Deep Learning and we are seeing, especially this year, attempts to optimise that, make things go faster and make them more repeatable.” But, as we all know, with great power comes great responsibility: “There’s this whole depth we are getting into, the expectations are so much higher, people don’t just expect it to work they expect it to work the way they want it to and in a way they can adopt.” So, with so much expected and required of Data Scientists in 2020, building the right team is more important than ever. However, many businesses, Vin believes, are yet to get their hiring processes right: “A lot of the measures that we use to sort of evaluate employees are fictional – when you say years of experience, it has no correlation to employee outcomes or the quality of employee you get long term. It’s the same thing as college degree, there’s no correlation.” So when Vin is trying to build a highly specialised team, what does he do? “We have to teach specialisation, we can’t expect it. We can’t bring someone in and call them a Data Scientist and hope that they train up. You end up with teams that are exactly the same because they have hired the same people, people who reinforce the bias of what they do, and that is where true leadership needs to come in.” A specialised team made up of individuals who bring their own ideas to the table is more important than ever, particularly as businesses demand more from their Data teams. Gone are the days of one-size-fits-all models. Businesses now want something tailored to them: “Custom models are huge. The “import from…” Machine Learning development from three years ago adds value when it comes to wrangling and doing the Analysis, but when it comes to creating models companies are now expecting it to become a competitive advantage. Companies no longer want the same model that everyone else has, now it has to be differentiating.” These smart, customised models, he adds, will help businesses through the current pandemic. “The best models right now are adapting rather than reacting.”  However, he’s sceptical about the Data Science community becoming too preachy:  “When it comes to COVID-19 one message I want to send to the Machine Learning and Deep Learning community is ‘shut up’. We don’t have the Data! We have so many Data Scientists talking about something that’s very important to get right. If you get it wrong the consequences and the credibility we will lose as a field is enormous.” Indeed, discussions about the lack of quality Data on COVID-19 are widespread at the moment and raise concerns for Vin: “What the last two and a half months has revealed is the danger of bad Data, the danger of assumptions that are hidden in Data that hasn’t been looked over well or wasn’t gathered well and was fed into these models that now aren’t robust. Of course, no model can account for something this drastic, but they should still be performing far better than they are right now.” Despite these concerns, Vin believes any change in the world brings about opportunities for those in the Data and technology space. “What I’ve been trying to do ever since I joined the technology space is figure it out. It’s constantly evolving and it’s constantly changing. That’s really what has driven my journey. I’m always trying to figure out ‘what’s next’ over the next five years, ten years whatever it may be.” If you’re looking for your next Data Science, Machine Learning or Deep Learning role, or want to build out your own highly-specialised team, we may be able to help.  Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.   

Ross Whatling

Ross Whatling

23. April 2020

The Search For Toilet Paper: A Q&A With The Data Society

We recently spoke Nisha Iyer, Head of Data Science, and Nupur Neti, a Data Scientist from Data Society.  Founded in 2014, Data Society consult and offer tailored Data Science training for businesses and organisations across the US. With an adaptable back-end model, they create training programs that are not only tailored when it comes to content, but also incorporate a company’s own Data to create real-life situations to work with.  However, recently they’ve been looking into another area: toilet paper.  Following mass, ill-informed, stock-piling as countries began to go into lockdown, toilet paper became one of a number of items that were suddenly unavailable. And, with a global pandemic declared, Data Society were one of a number of Data Science organisations who were looking to help anyway they could.  “When this Pandemic hit, we began thinking how could we help?” says Iyer. “There’s a lot of ways Data Scientists could get involved with this but our first thought was about how people were freaking out about toilet paper. That was the base of how we started, as kind of a joke. But then we realised we already had an app in place that could help.” The app in question began life as a project for the World Central Kitchen (WCK), a non-profit who help support communities after natural disasters occur.  With the need to go out and get nutritionally viable supplies upon arriving at a new location, WCK teams needed to know which local grocery stores had the most stock available.  “We were working with World Central Kitchen as a side project. What we built was an app that supposed to help locate resources during disasters. So we already had the base done.” The app in question allows the user to select their location and the products they are after. It then provides information on where you can get each item, and what their nutritional values are, with the aim of improving turnaround time for volunteers.  One of the original Data Scientists, Nupur Neti, explained how they built the platform: “We used a combination of R and Python to build the back-end processing and R Shiny to build the web application. We also included Google APIs that took your location and could find the closest store to you. Then, once you have the product and the sizes, we had an internal ranking algorithm which could rank the products selected based on optimisation, originally were based on nutritional value.”  The team figured that the same technology could help in the current situation, ranking based on stock levels rather than nutritional value. With an updated app, Iyer notes “People won’t have to go miles and stand in lines where they are not socially distancing. They’ll know to visit a local grocery store that does have what they need in stock, that they’ve probably not even thought of before.” However, creating an updated version presented its own challenges. Whereas the WCK app utilised static Data, this version has to rely on real-time Data. Unfortunately this isn’t as easy to come by, as Iyer knows too well:  “When we were building this for the nutrition app we reached out to groceries stores and got some responses for static Data. Now, we know there is real-time Data on stock levels because they’re scanning products in and out. Where is that inventory though? We don’t know.” After putting an article out asking for help finding live Data, crowdsourcing app OurStreets got in touch. They, like Data Society, were looking to help people find groceries in short supply. But, with a robust front and back-end in place, the app already live, and submissions flying in across the States, they were looking for a Data Science team who could make something of their findings.  “We have the opportunity,” says Iyer “to take the conceptual ideas behind our app and work with OurStreets robust framework to create a tool that could be used nationwide.” Before visiting a store, app users select what they are looking for. This allows them to check off what the store has against their expectations, as well as uploading a picture of what is available. They can also report on whether the store is effectively practising social distancing. Neti explains, that this Data holds lots of possibilities for their Data Science team: “Once we take their Data, our system will clean any submitted text using NLP and utilise image recognition on submitted pictures using Deep Learning. This quality Data, paired with the Social Distancing information, will allow us to gain better insights into how and what people are shopping for. We’ll then be able to look at trends, see what people are shopping for and where. Ultimately, it will also allow us to make recommendations as to where people should then go if they are looking for a product.”  In addition to crowdsourced information, Data Society are still keen to get their hands on any real-time Data that supermarkets have to offer. If you know where they could get their hands on it, you can get in touch with their team.  Outside of their current projects, Iyer remains optimistic for the world when it emerges from the current situation: “Things will return to normal. As dark a time as this is, I think it’s going to exemplify why people need to use Artificial Intelligence and Data Science more. If this type of app is publicised during the Coronavirus, maybe more people will understand the power of what Data and Data Science can do and more companies that are slow adaptors will see this and see how it could be helpful to their industry.”   If you want to make the world a better place using Data, we may have a role for you, including a number of remote opportunities. Or, if you’re looking to expand and build out your team with the best minds in Data, get in touch with one of expert consultants who will be able to advise on the best remote and long-term processes. 

Nick Mandella

Nick Mandella

14. April 2020

Top Ten Tips: Video Interviewing for Data & Analytics Professionals

Large parts of the world may have moved to working remotely for the foreseeable future, but that doesn’t mean that their projects have ground to a halt. And, with Data & Analytics at the forefront of many businesses ongoing strategies, their Data teams are continuing to grow regardless.  As a result, we’ve seen a huge increase in the number of video interviews taking place as companies look to continue hiring and meet their growth and business goals.  For many, however, video interviewing will be an entirely new experience, one that throws a number of complications into the mix during an already unusual situation. With that in mind, we’ve put together our ten top tips for acing a video interview: UNDERSTAND THE TYPE OF VIDEO INTERVIEW “Live” interviews are ones where you’ll see another person on the end of the connection. These are typically conducted using Skype, Zoom or Google Hangout. For some interviews you’ll be recording your answers, expect these to be done using sites like Sparkhire. Ask your recruiter or contact with the business in advance so you know what type of video interview to expect. TEST YOUR INTERNET CONNECTION AND WEBCAM Test your connection for Skype, Zoom Google Hangout, or whichever interview platform you are specifically using. Do a test run to see how fast/slow your connection is to see if you will have any problems with the video that you may need to resolve beforehand. SOUND CHECK  Equally as important is how you sound. Having to repeat your answer because the interviewer couldn’t hear you will not only annoy the interviewer, it may disrupt your flow and throw you off guard. If possible, try not to use headphones, as they may make you look less professional (video interview or otherwise!), but audio quality is more important than appearance here, so check the audio in advance to be sure. CONNECT WITH YOUR INTERVIEWER IN ADVANCE  If you know who you’re interviewing with connect with them on LinkedIn beforehand or get their phone information. This is so you have a backup in case the video platform isn’t working and will save any last-minute panicking if the platform isn’t working. DRESS THE PART Just because the interview is over video doesn’t mean you don’t get dressed up for it. Dress how you would if you were having the interview face to face – first impressions count! Plus if you’re dressed smartly from head to toe it’ll help you feel best set up for success. LOOK BEHIND YOU  Interviewers can easily be distracted by what is happening behind you. If you don’t have a home office, use a room where you’ll have a wall or bookcase behind you which will look professional. REMOVE DISTRACTIONS Noise, music, children and pets can all be distractions to you and your interviewer. Be prepared to continue through the interview if your pet makes noise or your child barges in. Ideally if you can find a quiet space away from these distractions you won’t be interrupted. MAKE EYE CONTACT Interviews over video won’t replicate a live meeting. You have to proactively make sure you smile, make eye contact and speak clearly. Don’t fidget or make a lot of movement – if the connection is slow, you’ll appear fuzzy and out of focus. DON'T PREPARE AT THE LAST MINUTE You wouldn’t leave preparing to the last minute if you were meeting face to face so a video interview shouldn’t be different. Prepare your answers, questions to ask the interviewer and use post it notes if you need helpful reminders for video-specific tips (Look at the webcam! Smile! Speak clearly!). KEEP A GLASS OF WATER NEXT TO YOU It’s an ideal prop if you do need to take a couple of seconds to collect your thoughts before answering a question. Don’t substitute for a hot beverage (tea or coffee for example) as if you do spill you don’t want to be distracted by a burn or stain.  If you are looking for your next role, we may be able to help. Take a look at our latest jobs, where you will find a number of remote working opportunities.  Or, if you are looking to make a remote hire, get in touch with one of our expert consultants and we can help you manage the process. 

Charlie Waterman

Charlie Waterman

31. March 2020

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