Milton Keynes, Buckinghamshire / £32000 - £50000
£32000 - £50000
Milton Keynes, Buckinghamshire
Up to £50,000
Hybrid - Milton Keynes/Manchester
A great opportunity for a Quantitative Analyst to join one of the UK's top banks and a global financial institution.
This role sits within one of the UK's top financial institutions and an international bank - as part of their IRB team. The IRB team focuses around capital management, which is an essential tool used in risk management and is used across the business to identify, assess and mitigate the risks involved in decisions made by the business. And in being a part of the team, you will be at the heart of understanding how risk are managed across the business.
Joining the team as a Quantitative Analyst, you will be responsible for developing, monitoring, and maintaining IRB credit risk models. You will also be required to provide data, analysis and support the team in answering queries from other areas of the business.
THE ROLE AND RESPONSIBILITIES
- Developing credit risk models via working with data and stakeholders
- Ensuring analysis and models are in line with requirements and regulations
- Contributing to the development and implement of new IRB models in the IT systems
- Producing and reviewing model monitoring reports
- Supporting model governance within the team
YOUR SKILLS AND EXPERIENCE
- Experience with SAS programming
- Expertise in Credit Risk Management
- Experience with model development
- Up to £50,000 + benefits
- Milton Keynes/Manchester
HOW TO APPLY
If interested in this role please send your CV to Jordan Victor via the Apply Link below
Data Analytics vs. Data Science: Which Should You Pursue? | Harnham Recruitment post
Businesses are recognizing the increasing importance of data experts to help the company grow. As a result, the hiring demand for Data Scientists and Data Management Analysts has grown by 46% since 2019. This projection will only continue to rise in the next few years. So if you’re planning to become a data analyst or a data scientist, then here’s what you need to know.Data Analytics and Data Science: What’s the Difference?Data Analysts and Data Scientists are both proficient in statistics and experienced in using database management systems. However, the key differences between these two professions revolve around their purpose for using the data.The Role of a Data AnalystThese professionals organize and examine structured data to create solutions that will drive a business’ growth. They are tasked with studying sets of data using various tools, such as Excel and SQL, to uncover insights and trends that will serve as an answer to certain queries. For example, they can provide data-driven answers that can explain your marketing campaigns’ conversion rates or improve the logistics of your products. Then, they present these findings to concerned individuals and departments so they can formulate strategies that would boost revenue, efficiency, and other improvements.The Role of a Data ScientistData Scientists are required to use their mathematical and programming skills to build statistical models that can provide solutions for a company’s potential problems. These professionals handle huge sets of both structured and unstructured data and prepare these for processing and analysis. They have to be very proficient in programming to utilize Predictive Analytics, statistics, and Machine Learning in unearthing meaningful insights from all the collected data. Their multidisciplinary approach towards data helps them draw conclusions that are valuable for specific business needs and goals.Career Paths for Aspiring Data AnalystsBusinesses, governments, and other institutions are on the search for individuals who are qualified in interpreting and communicating data. Data analysts are often offered huge salaries and great work benefits because the demand is so high and yet, the pool of talent is very limited.You can become qualified for a wide array of careers in data analytics through a comprehensive master’s degree program that will teach you how to interpret data and present actionable insights. These careers span from digital marketers to quantitative analysts. Graduates can work in governments and insurance companies as financial analysts who are in charge of assessing financial statements and economic trends to boost profit. On the other hand, you can also work as a marketing analyst whose responsibilities involve monitoring sales venues and evaluating consumer data. Their salaries range from $62,000 (Insight Analysts) to as much as $225,000 (highly paid Customer Analysts).Career Paths for Aspiring Data ScientistsData Scientists are experts in statistical analysis and in programming languages, such as Python and R. Thus, the average starting salary for professionals in this field is around $100,000 per year.Data Scientists would need to earn a bachelor’s degree and a master’s degree in computer science so that they would be adept at using complex software programs that are necessary for the position. If you’re more interested in software development, then you can work as a data engineer. These professionals create infrastructures that can gather and store data that analysts and other scientists may need to use. Data modellers, on the other hand, use techniques and databases to design and document data architecture.You can become a great asset to top companies in the US by pursuing a degree and a career in data analytics or data science. In this digital age, you can only expect that the demand for these positions would rise as data becomes increasingly important in driving business growth. Browse our fantastic data science jobs and data analyst jobs today. Written by Jena Burner for harnham.com
Is Product Analytics the new Digital Analytics? | Harnham Recruitment post
Following on from our exploration of what Digital Analytics is, and the exploration specifically of hiring Digital Insights Analysts in the North of England and Midlands, we wanted to take a look at Product Analytics, and how it differs from the standard Digital Analyst role.To help investigate the importance of Product Analytics in the current market, we have interviewed Nicky Tran, a Product Analyst at Virgin Media (Manchester).What Is A Product Analyst?In simple terms, a Product Analyst ‘’looks at the different products a company has, and then you are identifying which areas of the product can be improved or which areas can be optimised.” While Digital Analytics can inform the product lifecycle, the interesting aspect to this role is, that unlike a traditional Web Analyst role, it is more of a hybrid role. Nicky emphasised that it is ‘’an upcoming sector within the analytics community’’, providing an overlap between Digital Analytics, Customer Analytics and Data Science.The key skills and tools for this role are advanced SQL, Google Analytics, and AB testing. So how does this skillset differ from a traditional Web Analyst? Nicky suggests that while the core requirements are that of a Web Analyst, with a web role you would essentially just be using Google Analytics Data. However, as a Product Analyst, you would be using advanced SQL to access other data bases, and pull data from models, and therefore, “you are combining two sets of data to get a more insightful look”.Why Is Product Analytics Important, And Why Are They Now Becoming More Prominent On The Market?Similar to Digital Analytics roles, it is clear that with the impending digital transformation, companies are becoming increasingly data-led, especially with regards to their digital platforms (and products).As a result of the pandemic, the digital space is so much more important than ever before. Therefore, to stay competitive, and to really understand the products from the consumer perspective, companies have to provide the most personalised customer experiences to acquire and retain their consumers. As Nicky mentions, ‘It is definitely worth making an ‘inventory’ to see how to promote what you have – it is about personalising the customer journey’.What are employers looking for in a Product Analytics candidate?Product Analytics are great due to their hybridity. In the current market, where there are numerous jobs, and few candidates, a Product Analyst (technically strong in three areas) is a highly sought-after rarity.Businesses are becoming increasingly invested in Product Analytics and having a Product team that works alongside the Digital team can be beneficial; especially when companies need to stay competitive.What are Candidates looking for? Understanding the differences between a Digital Analyst, and a Product Analyst is key to understanding what a candidate is looking for. Nicky suggested that this Product Analyst role enabled her to be the ‘bridge’ between areas.So how does the future of a Product Analyst differ to that of the route of a Digital Analyst? For Nicky, this is one of the most important factors to being a Digital Analyst, as she has the option to go down the Data Science route in the future should she wish. The more technical skills she has as a Product Analyst means she is building up experience across different areas of Data & Analytics, giving her a slightly different career path, should she want to go down a more technical route.Why Choose A Product Analyst Role?“If you come from a technical background – maths, physics, computer science – and are interested in how the numbers are crunching, it is worth going into Product Analytics, as it needs a logical mathematics brain, to be able to convert it into a way which is useful to stakeholders.”From speaking to Nicky, it is clear that Product Analytics is an up-and-coming role that people don’t know enough about it. Therefore, if you are curious about Product Analytics, or any of the different roles the market has to offer at the moment, as an employer looking for help hiring, or a candidate actively or passively looking for work, Harnham can help. Take a look at our latest Product Analytics jobs, or get in touch for more information on how we can support your hiring needs.
As Incidents Of Cybercrime Increase, How Can A Fraud Analyst Give Your Business Peace Of Mind?
Whilst it’s true that cybercriminals are becoming more creative and sophisticated, as are analytical techniques and the experts that wield them. Fraud Analysts now have more techniques and reach than ever, and as incidents of cybercrime increase, this isn’t an area that businesses should be scrimping on.
According to PwC’s Global Economic Crime and Fraud Survey 2022, 46 per cent of organisations surveyed reported experiencing fraud or financial crime over the last 24 months and tech, media and telecommunications businesses appeared to have taken the brunt. Findings showed that nearly two-thirds of this group experienced some form of fraud, the highest incidence of any industry.
The ONS also recently released stats showing that fraud offences increased by 25 per cent in 2021 (to 4.5 million offences) compared with the year ending March 2020. Indeed, the proportion of these incidents that were cyber-related increased to 61 per cent up from 53 per cent.
The rise of cyber-fraud is a clear issue and for some businesses such as financial institutions, tackling this by using fraud teams made up of expert Fraud Analysts is the norm. But for others, it may not have been seen as a priority until recently. However, any business which has a growing number of online transactions will become a bigger target for fraudsters and would benefit from a team member able to help minimise the risk.
So, how can fraud analysts help?
Far from wanting to paint a bleak picture, while fraud techniques are evolving and improving, so are anti-fraud efforts. All risks associated with financial crime involve three kinds of countermeasures: identifying and authenticating the customer, monitoring and detecting transaction and behavioural anomalies, and responding to mitigate risks and issues. All of these are carried out by fraud experts, such as Fraud Analysts, armed with ever-evolving technologies and techniques. So, what exactly does a Fraud Analyst do?
Fraud Analysts will track and monitor transactions and activity, identify and trace any suspicious or high-risk transactions, determine if there is improper activity involved, and identify if there is any risk to the organisation or its customers. They are able to digest huge swathes of information and quickly and efficiently prioritise the data that’s important in order to tell a story of fraud or no fraud.
To cope with the speed and scale of online commerce, new technologies such as Machine learning (ML) models have come to the fore. These models have the ability to simulate thousands of scenarios and take over the mundane tasks of sifting through swathes of data in a tiny percentage of the time it would take a human. The systems used by Fraud Analysts will vary based on the industry, but a common example is rule-based expert systems (RBESSs). A very simple implementation of artificial intelligence (AI) RBESSs are used to detect fraud by calculating a risk score based on users’ behaviours, such as repeated log-in attempts or ‘too-quick-for-being-human’ operations. Based on the risk score, the rules deliver a final decision on each analysed transaction, therefore blocking it, accepting it, or putting it on hold for analyst’s revision. The rules can be easily updated over time, or new rules can be inserted following specific needs to address new threats.
This method has proved very effective in mitigating fraud risks and discovering well-known fraud patterns. That said, rule-based fraud detection solutions have demonstrated that they can’t always keep pace with the increasingly sophisticated techniques adopted by fraudsters, without regular updates and expert use.
Machines also cannot mimic human traits like intuition. People can detect if things aren’t right even if they have not seen them before. It’s an instinct not yet successfully trained into machines. Therefore, new trends are much better pursued by an analyst and then a machine can be trained to stop future occurrences. A well-implemented ML system will free up precious time for an analyst to perform these more productive tasks.
A non-stop process
So, your Fraud Analyst has now set up a new ML system to identify fraudulent activity and is also looking for new trends that fraudsters may be trying – now what? Fraud Analysts never sit still. Their job is not a one-time fix but one of constant evolution and refinement. Their role involves identifying weaknesses in systems and continually looking for opportunities for improvement, such as recommending anti-fraud processes to detect new patterns or new software tools to help with reporting. Their finger is always on the pulse of emerging developments and will ensure your company remains protected against current risks.
Not only is this aspect part of the job description, but it is also to some extent inherent to their nature. Fraud Analysts tend to be curious, have a strong attention to granular detail, as well as an inclination towards problem-solving. Leaving no stone unturned is part of their makeup. This analytical skillset will dig out any problems that are there – which will unfortunately then require you to fix them (sorry!) – but it is far better to be aware of any weaknesses now. The majority of companies only realise their shortcomings when it is already too late. Ultimately it is better to be safe than sorry.
A Fraud Analyst not only helps to protect businesses against creative cyber criminals but will also give owners reassurance as they look to grow and thrive unimpeded.
If you are looking for a complete recruitment solution across the breadth of Data & Analytics disciplines to build out a robust Data & Analytics function, get in touch with one of our expert consultants here.
Looking for a new role? Take a look at our latest Fraud Analyst jobs.
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