Things You Need To Know About Contracting



Career choices written on napkin

Whether you specialise in Credit Risk, Analytics, Data, Modelling, SAS, Insight, Data Management or Marketing Analytics, if you want to work on contract, this article will ensure you’re in the know.

The demand for contract workers continues to rise, especially within Data and Analytics, as flexible working becomes a way of life in many companies. Skills offered by professional contractors are highly valued by a wealth of organisations, many of whom see the procurement of short-term expertise as a key element of their recruitment strategy. Additionally, the growing skills gaps in Data and Analytics has meant that contract resource has become a necessity for many employers.



What is contract work?


What are the options?

Other considerations?

Build a network

What is contract work?

Contract work means that you are not employed on a permanent basis by anyone (apart from your own Limited Company). You have the freedom and flexibility to decide when you want to work and for how long. Generally within our sectors and industries of focus contract roles range from 3 month assignments to 2 year contracts.

In today’s competitive business, environment employers are turning increasingly to contract professionals to provide additional resources as and when they are needed, as well as securing harder-to-find skill sets for specific projects. In fact, for some Data and Analytics skills such as SAS Analytics, Web Analytics, Marketing Analysis & Credit Risk, there is a real shortage of talent and that’s where you come in.

In addition, the flexibility, choice and variety of experience you can achieve by fulfilling contract or interim positions mean that this is an increasingly popular career choice for many professionals. As well as the career choice, contract work can also be an ideal solution for those between permanent jobs, travelling or looking to return to work after a career break. 

What are the options?
 

There are two main options for contracting; you can either set up your own limited company or go through an umbrella company. Which one is for you? If contracting is a long term career choice then setting up your own Limited Company may be the best option as it is the most tax efficient way of managing your income. However, if you are only going to contract for maybe 3 – 6 month with the intention of then getting a permanent job, utilising an umbrella company will potentially be your best choice.

If you choose to set up a Limited Company, which is the most common route, then you will need to:

  • Become familiar with the constraints of IR35 and what contractual agreements you should and shouldn’t enter in to. If you are paying taxes like a Limited Company, you will need to behave like one, for example restrictions potentially exist concerning how long you can provide services for one client. Also, your Limited Company needs the right to substitute its employees with other consultants.
  • Get a good accountant. Don’t cut corners, you really should do everything thoroughly and by the book. This is a real hot topic for the Government at the moment and you need to be familiar with and comply with all the latest regulations.
Other considerations if your choose to contract

Think about the medium term when it comes to income, as a stable contract for 12 months at a market rate is far better than a very highly paid one for 3 months. It can take another 2 months to find that next contract and you could be unpaid for that length of time.

Be careful contracting through micro agencies (1 or 2 staff) if you can. If you do choose this route, you must credit check them. Some micro agencies can be financially risky, remember they are the ones paying you, not the client.

One of the big benefits of using agencies, as well as an easy route to market for you, is the fact they will normally pay you within 2 weeks of the end of the month you have just completed. Be aware that contracting for companies directly can, in many cases, mean you are paid like a supplier and monthly invoices can take around 45 days to be paid in the UK.

Build your network

Lastly, build your network with all agencies and clients, as well as other contractors.  However you choose to contract, you are now the sales function for you and your skills, and your network will have the have the knowledge of where your next contract is.


<< By Simon Clarke>>

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Weekly News Digest - 18th-22nd Jan 2021

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 & Analytics. KDNuggets: 20 core Data Science concepts for beginners The field of Data Science is one that continuously evolves. For Data Scientists, this means constantly learning and perfecting new skills, keeping up to date with crucial trends and filling knowledge gaps.  However, there are a core set of concepts that all Data Scientists will need to understand throughout their career, especially at the start. From Data Wrangling to Data Imputation, Reinforcement Learning to Evaluation Metrics, KDNuggets outlines 20 of the key basics needed.  A great article if you’re just starting out and want to grasp the essentials or, if you’re a bit further up the ladder and would appreciate a quick refresh.  Read more here.  FinExtra: 15 DevOps trends to watch in 2021 As a direct response to the COVID-19 pandemic, there is no doubt that DevOps has come on leaps and bounds in the past year alone. FinExtra hears from a wide range of specialists within the sector, all of whom give their opinion on what 2021 holds for DevOps.  A few examples include: Nirav Chotai, Senior DevOps Engineer at Rakuten: “DataOps will definitely boom in 2021, and COVID might play a role in it. Due to COVID and WFH situation, consumption of digital content is skyrocket high which demands a new level of automation for self-scaling and self-healing systems to meet the growth and demand.” DevOps Architect at JFrog: “The "Sec'' part of DevSecOps will become more and more an integral part of the Software Development Lifecycle. A real security "shift left" approach will be the new norm.” CTO at International Technology Ventures: “Chaos Engineering will become an increasingly more important (and common) consideration in the DevOps planning discussions in more organizations.” Read the full article here.  Towards Data Science: 3 Simple Questions to Hone Python Skills for Beginners in 2021 Python is one of the most frequently used data languages within Data Science but for a new starter in the industry, it can be incredibly daunting. Leihua Yea, a PHD researcher at the University of California in Machine Learning and Data Science knows all too well how stressful can be to learn. He says: “Once, I struggled to figure out an easy level question on Leetcode and made no progress for hours!” In this piece for Towards Data Science, Yea gives junior Data Scientists three top pieces of advice to help master the basics of Python and level-up their skills. Find out what that advice is here.  ITWire: Enhancing customer experiences through better data management From the start of last year, businesses around the globe were pushed into a remote and digital way of working. This shift undoubtedly accelerated the use of the use of digital and data to keep their services as efficient and effective as possible.  Derak Cowan of Cohesity, the Information Technology company, talks with ITWire about the importance of the continued use of digital transformation and data post-pandemic, even after restrictions are relaxed and we move away from this overtly virtual world.  He says: “Business transformation is more than just a short-term tactic of buying software. If you want your business to thrive in the post-COVID age, it will need to place digital transformation at the heart of its business strategy and identify and overcome the roadblocks.” Read more about long-term digital transformation for your business here.  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.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Weekly News Digest - 11th-15th Jan 2021

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. KDNuggets: 20 core Data Science concepts for beginners The field of Data Science is one that continuously evolves. For Data Scientists, this means constantly learning and perfecting new skills, keeping up to date with crucial trends and filling knowledge gaps.  However, there are a core set of concepts that all Data Scientists will need to understand throughout their career, especially at the start. From Data Wrangling to Data Imputation, Reinforcement Learning to Evaluation Metrics, KDNuggets outlines 20 of the key basics needed.  A great article if you’re just starting out and want to grasp the essentials or, if you’re a bit further up the ladder and would appreciate a quick refresh.  Read more here.  FinExtra: 15 DevOps trends to watch in 2021 As a direct response to the COVID-19 pandemic, there is no doubt that DevOps has come on leaps and bounds in the past year alone. FinExtra hears from a wide range of specialists within the sector, all of whom give their opinion on what 2021 holds for DevOps.  A few examples include: Nirav Chotai, Senior DevOps Engineer at Rakuten: “DataOps will definitely boom in 2021, and COVID might play a role in it. Due to COVID and WFH situation, consumption of digital content is skyrocket high which demands a new level of automation for self-scaling and self-healing systems to meet the growth and demand.” DevOps Architect at JFrog: “The "Sec'' part of DevSecOps will become more and more an integral part of the Software Development Lifecycle. A real security "shift left" approach will be the new norm.” CTO at International Technology Ventures: “Chaos Engineering will become an increasingly more important (and common) consideration in the DevOps planning discussions in more organizations.” Read the full article here.  Towards Data Science: 3 Simple Questions to Hone Python Skills for Beginners in 2021 Python is one of the most frequently used data languages within Data Science but for a new starter in the industry, it can be incredibly daunting. Leihua Yea, a PHD researcher at the University of California in Machine Learning and Data Science knows all too well how stressful can be to learn. He says: “Once, I struggled to figure out an easy level question on Leetcode and made no progress for hours!” In this piece for Towards Data Science, Yea gives junior Data Scientists three top pieces of advice to help master the basics of Python and level-up their skills. Find out what that advice is here.  ITWire: Enhancing customer experiences through better data management From the start of last year, businesses around the globe were pushed into a remote and digital way of working. This shift undoubtedly accelerated the use of the use of digital and data to keep their services as efficient and effective as possible.  Derak Cowan of Cohesity, the Information Technology company, talks with ITWire about the importance of the continued use of digital transformation and data post-pandemic, even after restrictions are relaxed and we move away from this overtly virtual world.  He says: “Business transformation is more than just a short-term tactic of buying software. If you want your business to thrive in the post-COVID age, it will need to place digital transformation at the heart of its business strategy and identify and overcome the roadblocks.” Read more about long-term digital transformation for your business here.  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.

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.

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