Sam Maughan - Recruiter Profile | Harnham Recruitment

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Sam Maughan

Senior Manager - UK

Bio

Sam joined Harnham in 2014 as part of the Risk Analytics team. After two years he relocated to New York to help set up Harnham’s Risk Analytics presence in the US. After a successful two years Sam returned home to the UK where he now manages the major accounts team at our Wimbledon headquarters!

Harnham blog & news

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

Visit our Blogs & News portal or check out our recent posts below.

Weekly News Digest: 4th-8th Jan 2021

Happy New Year! This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of data and analytics. TechRepublic: How IT can prepare for the coming hybrid work environment As the world continues to feel the pressures of COVID-19 , remote working is no longer the temporary and novel approach to work that we had envisaged. Vaccines are being approved and healthcare professionals are supporting its rollout across the globe. And, as each dose is administered, we move one step closer to what is likely to become a hybrid working situation. It is therefore pressing for tech leaders to prepare for a shift to this style of work. TechRepublic have explored how these leaders need to ensure that their technology is agile enough to support the needs of the workforce. Yet they also need to look beyond the tech, to redefine how teams work together. Read the full article here. Forbes: 350 CMOs: 3 Marketing Supertrends For 2021 ... And The No-Hype Future Of Marketing Tech We’re a big fan of this piece from John Koetsier, writing for Forbes. He describes how the marketing trends of the year ahead will take a focus on the holistic transformation in a digital-first world. Drawing on the thoughts of a range of Chief Marketing Officers, Koetsier explores that a mixture of new, emerging technologies will see the evolution of marketing to put digital right at the core. Openpath CMO Kieran Hannon, “Now meaningful customer-centric digital transformation can accelerate.” Suzanne Kounkel, Chief Marketing Officer for Deloitte, “Fusion is the new ecosystem. Fusion is the art of bringing together new business partnerships, customer insights, and digital platforms to create ecosystems.” Tristan Dion Chen, CMO of University Credit Union, “It is without a doubt crucial to recognize how COVID-19 has ushered in a strong sense of empathy as a driving force within the marketing industry.” The marketing industry is set to experience continued innovation and growth. Read more on this here. ZDNet: Facial recognition: Now algorithms can see through face masks Last year was a year unlike any other. The complete shift in the way we have had to go about our day-to-day lives, brought about by the ongoing implications of the COVID-19, is still being felt now. One of these changes to our lives is the compulsory requirement to wear a face mask when leaving home. Now, of course, this requirement has brought up some challenges for using our technology, such as banking and payment applications, which need facial recognition to activate it! However, ZDNet have reported that algorithms can now see-through face masks (pretty sweet, right?) The US Department of Homeland Security has carried out trials to test whether facial recognition algorithms could correctly identify masked individuals. This could be a real support for travel, banking and mobile technology in the future. Read more on the trial here. Towards Data Science: Predicting the outcome of NBA games with Machine Learning The NBA season is back and well underway. Will the Los Angeles Lakers take the top spot again this year? Lots of fans will be making their own predictions as the season begins, but new research has been used to help predict the outcome of NBA games – with the help of the insightful tech that is machine learning. Focusing on five core steps, the team at ‘Towards Data Science’ used Big Data Analytics to help them predict the outcome of games: Scraping Relevant DataCleaning and Processing the DataFeature EngineeringData AnalysisPredictions Through the research, they found that the best published model had a prediction accuracy of 74.1 per cent (for playoff outcomes), with most others achieving an upper bound between 66–72 per cent accuracy. That’s scarily good! Click here to read more on the study and see the statistics in action. We've loved seeing all the news from Data and Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

by Sam Maughan

08. January 2021

Trade Analysts Keep Money Flowing on the Field

What if you could manage risk and build a winning team the way Billy Bean does in Moneyball? If you’ve never seen the movie, it’s essentially this. You don’t need the best to win, the players who will cost you the most money or who are the most popular. You need players whose sole skill is to get on base. When it comes to the world of finance, how might you manage risk and find ways to get on base so to speak? You may want to consider a Trade Analyst. Conversely, if you’re a data professional who’s got a nose for numbers, predictions, and the aptitude to get on base yourself, you may want to consider this as your next role. Not unlike so many Data Analyst jobs, you’re using Data to determine risk as well as deep dive into SWOT (strengths, weakness, obstacles, and threats) for your business. You’ll be managing statistics and pinpointing the best times of the day for optimal trading. A Key Player in the World of Trade Much like a stockbroker begins when the markets open, so too, does a Trade Analyst. Your mission, should you choose to accept it, is to run point between the stockholders and those for whom they’re buying and selling. Looking for puzzle solvers with an eye for detail and investigation, this role offers work with people from around the world. And as we continue, or as this year comes to a close, begin to cement our remote working opportunities, the world opens a host of opportunities for this role and many like it. What You Need to Know Buzz words abound in the data space and the classification for Trade Analyst can also be Financial Services Agent. Perhaps FSA is better as it gives a much more concise idea of what the job entails. However, Trader Analyst likens to a version of a Stock Broker who can drill down to the sharpest point what works, what will sell, what won’t, and how to fix what won’t work to what will. While education is important for this role, the soft skills so in demand will be required here, too. Can you be the calm in the chaos? Does making the sale motivate you? Can you think on your feet? If you answered yes to any of these questions, here are a few education and skills components you’ll need to know. Degree in international business is a good place to start as is a degree in finance, economics, or logisticsAdd in a second language for good measureStrong research skills.Understanding financial trends within and across geographic regionsUnderstanding supply and demandHighly communicative with staff, executives, stakeholders, and the public. Not unlike a language professional who roles easily from a foreign language to English and back again, a Trade Analyst must be able to translate numbers and predictions into the language of persuasive bargaining. Market analysis conducted through such platforms as polls and surveys. This role offers job security for the professional who comes alive in a fast-paced environment within the world of business. Your wallet and bank account may thank you, too. Going to the Show In baseball, going to the show implies you’re in the major leagues. That you’ll perform on the field of a major league team. You’re officially ‘on stage’. And so, it is with your role, even entry-level, of a Trade Analyst. From the moment you’re in the office and the phone rings to the final closing bell of the exchange, you’re on the field, and playing with the heavy hitters. You’ll identify risk, engage with customers, pay attention to the score, er deliverables and expectations, all the while staying in compliance with regulations. If you’re looking for a role in Data & Analytics or are interested in finance or international trade analysis, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to learn more.

by Sam Maughan

19. November 2020

Three Surprising Skillsets for Data Professionals

Data Scientists today must be more than the sum of their parts. Businesses who wish to move forward are looking for more industry-specific focus when searching for the right candidate. Finding the right professional who not only writes code but can also speak to the Data once it’s analysed to business executives is becoming more important than ever. The title and skills required have expanded and the once ‘unicorn’ aspect of having both the technical and soft skills is no longer rare. It’s essential. Data Professionals In Demand As everyone moved online, many businesses sat up and took greater notice. Without a Data team today, it’s nearly impossible to conduct business as usual. Add in Machine Learning development and application, Deep Learning and Reinforcement Learning for AI, and Neural Network Programming to name a few, businesses need to have professionals in place who can conceive, develop, and implement these applications. So, now that businesses can see the importance of having everything in one place, what are they looking for in a candidate? Data Engineers and Data Architects are the most sought after. After all, these enterprises need to have professionals on board who can lay the foundation on which to build the Data first. While knowledge of technologies such as Python or Kubernetes is essential, talent with a twist of hybrid experience in Software Engineering is a boon for career advancement. Niche skill sets within the applications of Machine Learning such as Reinforcement and Deep Learning are highly desired. Data professionals with an industry-specific focus are quickly becoming the go-to resource for many businesses. They need people who understand not only their business but also how properly processed Data can affect it. Three Skillsets for a Post Pandemic World While Data Literacy and Tech Savvy are probably the first skillsets which might come to mind, here are a few more which may not seem quite as obvious. Critical Thinking and Leadership – With nearly everyone online, it will be imperative for professionals who can lead in a linear fashion. As the gig economy expands and teams become more fluid, different people will have the opportunity to lead at different times. The hierarchal structure is devolving to shared leadership opportunities in which everyone is allowed to shine. Collaboration will be key among remote teams around the world. To that end, critical thinking and the ability to separate fact from fiction will be highly regarded. Objectivity will help businesses ensure the right business decisions are being made from an informed team. Emotional Intelligence – EQ has risen into the list of soft skill requirements highly desired. Those self-aware individuals who cannot only express and control their emotions but be aware of others’ emotions may also find themselves in leadership positions. Their compassion and camaraderie within their teams can produce projects more effectively and efficiently. And with the majority of talent leaving jobs due to poor management, emotional intelligence may focus Hiring Managers on what to look for in leadership candidates outside their technical skills and seniority levels. Creativity & Innovation – It may seem as though there isn’t much place for Creativity and Innovation among AI, robotics, automation, Big Data, Data Science, and Data Technology. Yet, now, it’s more important than ever and it’s those same verticals which allow greater creativity. Consider the shift of car manufacturers to ventilator manufacturers or the apps which allow Telemedicine to exist in our world of social distancing. These are just a couple of examples of human ingenuity. People will always need dreamers, inventors, and creators to develop products and services to make life easier. In our recently released 2020 Salary Guide we discuss each specialism, what’s working and what isn’t. And how businesses can hire and retain top talent to keep their projects on track and their businesses running smoothly. If you’re interested in Data and Technology, Risk or Digital Analytics, Life Sciences Analytics, Marketing & Insight, Data Science, or Computer Vision, take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

by Sam Maughan

27. August 2020

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.

by Sam Maughan

14. May 2020

NADAL OR DJOKOVIC? COULD YOU PREDICT THE WINNER?

Most pundits will have an opinion on who will triumph in this year's US Open men's final - Rafael Nadal or Novak Djokovic - but the best insights into who will be crowned champion will come from the same technology that has helped cities to lower crime rates and plan for extreme weather.Deep in the bowels of Arthur Ashe Stadium in Flushing Meadows, Queens, New York, beats the data heart of the 2013 US Open.In a bland room accessed through an unmarked door, more than 60 laptops are piled high, arranged like a command control center for a mission to the moon.This room is known as "scoring central", according to US Open officials.It's where data is pushed to scoreboards on Louis Armstrong Court - the second largest US Open tennis court - or to TV screens across the globe.But more than a power processing center, this is where the results of matches are broken down and analyzed, where it's determined not only who won, but why they won, according to the numbers."Say you wanted to see every backhand unforced error in a match. You would touch a button and all of those would come up," says IBM's vice-president of sports marketing, Rick Singer.But seeing what has happened in past matches is rapidly giving way to better predicting what will happen in future pairings, explains Mr Singer.To put it simply: the past might have centered around intuitively understanding that a player who gets a majority of their first serves in will win the match.The future is pinpointing the exact percentile threshold the player must cross to win.'Unusual statistics'This year, IBM has gathered more than 41 million data points from eight years of Grand Slam tennis matches to better understand the small details that end up deciding a match.Djokovic will win if he:Wins more than 57% of 4-9 shot ralliesWins more than 39% of first serve return pointsHits between 63% and 73% of winners from the forehandNadal will win if he:Wins more than 48% of 4-9 shot ralliesWins more than 63% of points on first serveAverages fewer than 6.5 points per game on his own serveSource: IBM The idea is that by crunching more and more data, patterns will emerge that can help better hone predictions.So what should Novak Djokovic do if he wants to beat a resurgent Rafael Nadal, who has emerged this summer as the dominant force on hard courts?Looking at data from the head-to-head matches between the two in Grand Slams, IBM says that if Djokovic wins more than 57% of medium-length rallies (of between four and nine shots) then he will emerge triumphant.He also has to win more than 39% of return points on Nadal's first serve.Nadal, on the other hand, has to dominate on his serve. If he wins more than 63% of points on his first serve then IBM predicts he will win.However, the longer Nadal's service games go on, the less likely he is to win. He needs to keep his service games relatively short, averaging fewer than 6.5 points per game, according to IBM."It's the same sort of statistical analysis and predictive analytics that we do for our clients all around the world, just applied to tennis," explains Mr Singer."What we're trying to do is find statistics that are unusual."A backhanded solutionDjokovic, for instance, must focus on getting his backhand into play.According to IBM's data, when Djokovic can hit his backhand deep to Nadal's forehand, his odds of winning the point dramatically increase.However, during this tournament that stroke has been particularly difficult for Djokovic - he's had 32 backhand winners, but 70 backhand unforced errors.For Nadal, he will go into the final knowing that his most powerful weapon - his forehand - is working well. He has hit 113 forehand winners, compared with Djokovic's 73.He will also know that as long as he can continue to keep up his variety of serve, and go to the net occasionally - where he's won 81% of the points he has played there - he might have the upper hand over Djokovic.Serbia's world number one will also have to improve his consistency in the final. Although both players have hit the same number of winners in the tournament so far (206), Djokovic has made 167 unforced errors, far more than Nadal's 130.And with the Spaniard having dropped serve just once all tournament, Djokovic will have to be more ruthless when taking any break point opportunities that come his way, having converted only 44% up until now.Elephant brainIt's only with the advent of big data technologies and faster, better, processing power that companies like IBM say they've been able to quickly and cheaply gather these new insights.Most of these big data crunching technologies, from predicting airline prices to sports champions, use something known as Apache Hadoop.Designed by engineers who had been working at Yahoo and elsewhere ("Hadoop" was the name of one of the creators' son's toy elephant), it is now just one of the components of IBM's predictive analytics toolkit.The hope is that in the future, statistics like these might not just be of benefit to sports as a whole, but that athletes themselves will be better able to calibrate their performances."Each tournament we evolve a little bit further," says Mr Singer.The goal, he says, is "to take the statistics beyond what people are expecting".But for fans watching the US Open final who have no head for statistics, Rafael Nadal's coach and uncle, Toni Nadal, has this simple advice for what it takes to succeed: "You should play good, nothing else. You should play very well." Click here for the article on the web.

by Sam Maughan

21. January 2015

EDWINA DUNN AND CLIVE HUMBY TO JOIN STARCOUNT

The husband-and-wife team who made more than £90m from helping Tesco understand what customers put in their shopping baskets are turning their hands to tailoring brands to individual users on the likes of Twitter and Facebook.Edwina Dunn and Clive Humby – who sold their Dunnhumby business to the supermarket giant for £93m – are to join the board of social network analytics firm Starcount.The pair are selling their private vehicle, H&D Ventures, to Starcount for an undisclosed sum, taking a stake in the business as part of the deal. It comes alongside a $5m (£3.25m) fundraising for Starcount, with new investors including Artemis Capital and Praetura Capital.In addition, Christopher Carter, former vice-chairman of institutional securities at Morgan Stanley, is joining Starcount’s board as chairman. He is also one of the new investors in what is the company’s third equity fundraising. Starcount is run by chief executive Drew Thomson, who remains in the role.Ms Dunn and Mr Humby set up H&D Ventures 18 months ago to continue their business interests in data mining following their exit from Dunnhumby. Since then, they have worked on a mobile data project for one of the country’s largest mobile networks, and looked at the different ways of using data from social networks. Ms Dunn described the move as her and her husband’s “big play”, referring to it as a “quantum leap” from their work with Tesco.She said that brands and companies tended to be poor at marketing to individuals via social networks.“In a way, we’ve gone back to mass marketing – everyone gets the same tweet or blog,” she said. “With the data that are available, we can look specifically at the people who influence others, and we can make ambassadors of them for brands and celebrities.”The duo began working with Tesco in 1995, helping use its Clubcard loyalty scheme to understand what its customers were buying, and how to attract them back to its stores. They stepped down from executive roles at Dunnhumby at the end of 2010. Click here for the original article on the web.

by Sam Maughan

08. August 2013

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