Our Awards




APSCO Awards for Excellence 2018

Harnham were named Recruitment Company of the Year £10m-£50m at the Apsco Awards for Excellence.

These prestigious awards are the highlight of the recruitment calendar and have become the premier showcase for innovation, best practice and outstanding achievements. Harnham are incredibly proud to have won this award, reflecting our tireless drive to achieve excellence within service, delivery, training and development.

Harnham APSCO Awards for Excellence 2017

Linkedin Most Socially Engaged Staffing Agencies 2018

Out of over 38,000 staffing firms based in the EMEA region, Harnham were ranked 6th on the list, which is based on certain key social metrics such as social reach, employee engagement, employer brand and content marketing. Read our COO Dave Farmer's thoughts on the award here.

Harnham Linkedin Most Socially Engaged Staffing Business

Merton Business Awards 2018

These awards are very close to our heart. Since 2006 our UK headquarters have been based in Wimbledon, Merton and in 2018 we won two awards - Best Business Over 50 Employees and Healthy Workplace.


Marketing & Digital Recruitment Awards 2018

The MDRAs recognise recruitment firms that have displayed unparalleled excellence within the marketing, advertising, creative and digital sectors. Harnham were winners of Best Data & Analytics Specialist Team for our exceptional service delivery within the Digital and Marketing sectors. 


Marketing & Digital Recruitment Awards 2017

THE SUNDAY TIMES TOP SMALL COMPANIES TO WORK FOR 2019

Harnham placed 26th in The Sunday Times Top 100 Small Companies To Work For 2019, whilst also achieving a three-star accreditation from Best Companies, the highest available.  


Harnham Staffing Industry Analysts Awards 2014

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.

Data & Analytics in Munich

Three Reasons Why Munich Is The Place To Be For Data Analysts

As one of the world’s largest economies, Germany continues to attract tech talent from all over the world, and has even overtaken the UK in terms of intra-Europe tech immigration in recent years. Whilst Berlin may be the first place that comes to mind when thinking of places to live as a Data Analyst in Germany, with its numerous start-ups and international culture, there are several reasons why you should also consider the southern gem of Munich. Here are three of the best: A First-Class Quality Of Life While the first thing that comes to mind when thinking of Munich is often the world famous Oktoberfest and the beer induced crowds packed into small beer tents paying the equivalent of a year´s salary for a pint, this is not the only thing Munich has to offer. During the other 349 days of the year when Munich is not packed with Lederhosen-wearing crowds from all over the world, it is a tranquil, green place to live.  Munich is home to a number of large parks, including the beautiful Englischer Garten, Museums and a number of non-beer related cultural events throughout the year. It’s also the third largest city in Germany and, as such, has all the benefits that big city life has to offer. However, nature is never far away, with a beautiful mountain landscape just on the horizon, including the tallest mountain in Germany, the Zugspitze, which sits only 90 km away. On top of this, the transportation system in Munich is one of the best in the country; clean, efficient and so simple to use, it actually makes commuting bearable.  Expansive Opportunities  Most major European cities have seen a boom in the tech market in recent years and Munich is no exception. Not only home to some of the biggest global and German players such as Amazon, MunichRE, Man, Allianz and Linde, the city is also seeing an increasing amount of investment in tech start-ups.  This has led to tech talent, particularly Data & Analytics talent, being highly sought after by a number of the country’s biggest and best employers. And healthy competition means even healthier salaries. Even though Munich doesn’t have the lowest cost of living around, the average pay for Data Analysts is higher than in most other German cities, meaning you’ll get to make the most of your time away from the office.  A Thriving International Culture With 25-38% of Munich´s residents originating from other nations, more and more companies, big and small, are open to welcoming English speakers into their teams. While the culture in Munich still makes it easy to immerse oneself into the German language and culture, the city is also very welcoming to its international inhabitants.  Of course not everyone can speak English, but it is surprising how many people do. This makes getting around as a non-German speaker that much easier, especially considering that the Bavarian version of German can sometimes feel like a completely different language to what is spoken by the rest of the country.  Like every country, different cities attract different personalities and find the right place for you is crucial before making a move. But, with its high quality of life, great job prospects and international culture, Munich certainly has a lot to offer for any Data Analyst looking to move to or within Germany.  If you’re considering making a move to Munich, take a look at our latest opportunities, or get in touch and we can discuss what could work best for you. 

Our New Berlin Office

We've launched two new offices

I'm incredibly pleased to announce that this week we have launched two new offices.  The first, in central Berlin, will solidify our on-the-ground presence in the German capital and allow us to continue to develop our client base in this rapidly-growing market. Run by Senior Manager Peter Schroeter, under the guidance of our Director of Europe, Alex Hutchings, we're really excited to see the new space become a hub for Berlin's Data & Analytics talent.  Secondly, we've also opened a second Wimbledon office. Despite only moving in to our current home 18 months ago, our rapid growth has led to us opening an additional Executive Office to house our Operations team as we bring in more and more expert consultants. Fortunately, it's just over the road, so there's no need to grab a bus between meetings.  This continues to be a great time for Harnham and watch this space for more growth news in the not too distant future. 

Machine Learning: How AI Learns

Machine Learning: How AI Learns

Amazon has begun curating summer reading lists. How? Patterns. Facebook shows you ads for items you may have been searching for online. How? It learns from your browsing habits. Ever wondered how Facebook knows you were just looking at that pair of shoes or that particular guitar. The Data you feed it, feeds its brain. In other words, this is how Artificial Intelligence learns. Machine Learning. Whilst it can be disconcerting to know that a machine understands our buying habits, that’s not the only thing it’s used for. It’s also a pivotal tool in such areas as Bionformatics, Biostatistics, Computational Biology, Robotics, and more.  What is Machine Learning? Ultimately, it’s a method of Data Analysis which helps to automate model building and is part of Artificial Intelligence. In other words, it helps to solve Computational Biology problems by learning from Data to identify patterns and make decisions with little human intervention. This helps scientific researchers learn about many aspects of biology. However, running a Machine Learning project can be difficult for beginners, who may experience issues when trying to navigate the information without making mistakes or second guessing themselves. This is one of the reasons a Computational Biologist might want to upskill with a course or two in Machine Learning for a more robust understanding of the information being learned and applied.  The Good News and the Bad With each shift of industrial revolution, there has been one system which has made an indelible mark on our daily lives and the Fourth Industrial Revolution is no different. Just like we can no longer imagine factories without assembly lines, we can also no longer imagine not having Siri, Google Maps, or online recommendations. But, as exciting and as important as these things are, Machine Learning has become so crucial to our daily lives, so complex, it takes a technology expert to master it leaving it nearly inaccessible to those who could benefit from it. Why is Machine Learning Important? By building models to peel back the layers and discover connections, organisations can more easily and more quickly make improved decisions with little to no human intervention. Computational processing is both more affordable and more powerful. It’s possible to quickly scale and produce models which can analyse bigger and more complex data and there’s also a chance to identify opportunities and to help avoid any unknowns such as risk. Machine Learning is used in every industry from Retail to Financial Services to Healthcare. Here are just a few ways it has already transformed our world. Retail – Retailers are able to learn from their customers buying habits, predictive buying habits, how to personalise a shopping experience, price optimisation, and customer insights.Financial services – Machine Learning helps to prevent fraud and identify Data insights.Healthcare – Wearable devices allow for real-time data to assess a patient’s health. Medical professionals can also more quickly find red flags which can help improve diagnoses and treatment.Oil and gas – It cannot only help find where oil might be, but also predict refinery sensory failure, and streamline distribution.Transportation – Help to make routes more efficient and predict problems that could affect the bottom line. While humans can create at least one or two models a week; Machine Learning can create thousands.  Ultimately, the goal of Machine Learning is to understand the structure of Data. As it learns to determine what Data is needed for its structure, it can be easily automated and sift through Data until a pattern is found. This is how machines learn. If you’re looking to take your next step in the field of Machine Learning, we may have a role for you. Take a look at our latest opportunities, or get in touch to see if we can help you take that next step.

How to get ahead in Risk Analytics

How To Get Ahead In Risk Analytics

In the world of Risk Management, top talent is always in high demand. Despite this, those who specialise in area know that progression can be difficult and, according to our 2019 Salary Survey, is the slowest in the industry. So, how can you differentiate yourself from the competition, and what steps can you take to make yourself the ideal candidate for that promotion or new job you’ve been hunting?  Whether you are looking to move somewhere new, or trying to climb the ladder in your current company, here are some ways that you can make sure you stand out.  Stay one step ahead in tech Traditionally, the Risk Analytics tech stack has comprised of SAS, SQL and VBA. SAS and SQL remain very much present, but we are also seeing a clear increase in the use of Open Source programming languages, such as R and Python. Unsurprisingly, a lot of Risk Modellers and Analysts are now spending their time in developing their skills in these languages. One might argue that if you know one language, there’s not too much work required to upskill in another when you take on a new role, but this isn’t necessarily true. By being proactive and evolving your skillset in your current position, your CV will have a much bigger impact when it lands on a Hiring Manager’s desk.  Over the past few years, we’ve also seen the arrival of Machine Learning and AI in the world of Risk. Whilst many businesses are still slow to embrace these technologies, do not be surprised to see them make a big impact over the next couple of years. Risk Analytics are catching up to the rest of the industry in regards to technology, and having the knowledge and skillsets required in these areas before they take off will only enhance you profile.  Business-driven Data  In the world of Risk Analytics, it is easy to think that if you have the right programming and analytical skills in the right tools, you’ve all got all you need. You might be off to a really good start, but there’s more to it than that. It about having the balance.  Yes, being data-driven and understanding complex model development is crucial to becoming a good performer in this industry but, what truly separates the good from the great, is business acumen. The ability to understand both what your analytics and models do, and how they impact the overall business is now at the top of most Hiring Manager’s lists.  A person with good quantitative skills will always see something that can be improved, but they also need to know when to stop and be happy with the result. The key to getting this right lies in their understanding of the business and the ability to answer questions like “If I sit and work on this for 8 more hours, will the real-world difference be worth that amount of time and resource?”. By viewing things through the prism of cost vs reward, and understanding that balance, you can demonstrate that your value to a business goes beyond your analytical skills.  React, adapt and attract In this world there are a few things we can take for a certainty; the sky is blue, it will rain on your day off, and there will always be new regulations for financial institutions. Because of the certainty of change, a key thing employers look for in candidates is the ability to react quickly and make changes as soon as they are needed. Fast growing companies such as Klarna, tink and iZettle may seem like fairy-tale success stories, but the real edge they have is their adaptability and agile culture. Whereas some traditional corporations and banks have lengthy and complicated processes required before they adapt to new regulations, these new companies embrace their agility and get things done.  The ability to be agile and adaptable is, therefore, something that a lot of businesses are starting to realise is key. Therefore, if you’re looking to get ahead, you should try to evolve these qualities in your working ways. If you are looking for something new, look to prove you are driven and do not fear change. If you can demonstrate that you are able to work with a business-oriented mindset and embrace change, you’ll stand out as a key player in your team.  Specialist vs Generalist  With the world of Risk Management offering a number of opportunities to become very specialised in very niche areas, it’s worth considering whether this approach is right for you. There are some definite pros, for example, if you are the best developer of PD models for non-retail, you will be highly sought after for roles in this area. Plus, high demand, and a shortage of skillsets means that you will be in a good position to seek a high salary and lots of benefits. However, this does mean that you are likely to only have the opportunity to work in this area for the foreseeable future and, for some, this can become repetitive and not provide enough of a challenge. Additionally, if you were ever were to apply to work in a new area because of this, you would likely find yourself overpriced and needing to take a step down in seniority.  The alternative, therefore, is to become more of a generalist, with a broader, but less advanced skillset. Think being able to play every instrument, but only knowing one song. There are definitely some clear benefits with this approach, not least the ability to work on a diverse set of projects, gain an excellent understanding of how Risk Management affects a business on every level, and be able to slot into a number of roles easily. You will also gain a better idea of which areas of Risk that you like, and which parts you dislike. Whilst many analysts begin as generalists before looking to specialise when they get promoted, they often find that their knowledge will not be as deep as their specialist counterparts. Therefore, it is likely they will have to take a step-down or make a sideways move before they can achieve that promotion.  There is no right or wrong when it comes to the specialist vs generalist argument. However, for those looking for faster progression early-on, a generalist approach may be better suited despite the fact that you may need to change approach before reaching the most senior levels.  Whilst demand will always be high for the best candidates, competition for promotions and senior roles in Risk Analytics remains fierce. Therefore, by proactively thinking about the ways that you work, how effective you are, your business focus, and what your ambitions are, you should be able to get the most out of your career.   If you’re looking to get ahead in Risk Analytics, we may have a role for you. Take a look at our latest opportunities, or get in touch to see if we can help you take that next step. 

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