Risk Analytics Recruitment

Specialist Risk Analytics recruitment from Harnham. Whether it's a contract, permanent or remote position, we find great people great Risk Analytics jobs.



recruiting RISK ANALYTICS SPECIALISTS  aCROSS bankING & fINANCIAL SERVICES

We appreciate the importance of protecting the world’s interests and know the role that data has to play. Our Risk Analytics team fully understands financial risk and decision science, as well as the significance of knowing your market.

RISK ANALYTICSrecruitment

As within the world of Risk Analytics, the customers we work with differ by location, size and industry.

We have experience across all the major marketplaces and can assist you, whether you are hiring to reinforce an existing presence or build a new one. As global leaders, we operate in four offices globally, recruiting for roles across the UK, US, Benelux, France, Germany, the Nordics and Spain. 



PERMANENT, CONTRACT & REMOTE risk analytics RECRUITMENT

We have one of the largest talent networks of Data & Analytics are uniquely placed to offer bespoke solutions whatever your hiring needs are.

Our tailored approach means we are able to deliver best in class Data Scientists every time. Whether it’s entire project teams, remote hiring and onboarding or just filling in the gaps, we have a solution to fit your requirements. Gain access to one of the largest talent networks of Data Scientists across the UK, Europe and the US.


Latest Jobs

Salary

£50000 - £65000 per annum

Location

Milton Keynes, Buckinghamshire

Description

This organisation operates within the finance and accountancy sector, offering their customers a community as well as the chance to grow, develop and upskill.

Salary

US$160000 - US$200000 per annum + Competitive Benefits

Location

San Diego, California

Description

A growing fintech start up addressing some of the world's largest problems in the credit space.

Salary

£65000 - £70000 per annum + Bonus

Location

London

Description

Data Science role in the asset management space

Salary

800000kr - 900000kr per annum

Location

Stockholm

Description

You will be managing a team of 3 data scientists and 1 user researcher, and partner with other product area managers on the Product area's goals.

Salary

£65000 - £85000 per annum

Location

London

Description

This is an exciting new opportunity for a Senior Data Scientist to join a successful product company!

Salary

US$200000 - US$220000 per annum

Location

San Diego, California

Description

Lead a growing team of data scientist and analysts at a fintech start-up!

Salary

£550 - £650 per day

Location

London

Description

Machine Learning Engineer (Contract) - InsurTech NLP, Python, Sentiment Analysis, SQL £550 - £650.day (Inside IR35) Remote Initially/London

Salary

£50000 - £60000 per annum

Location

Birmingham, West Midlands

Description

Join a leading telecoms company where you will responsible for building end to end predictive models

Salary

£550 - £600 per day

Location

London

Description

Machine Learning Engineer (Contract) Python, Algorithms, Machine Learning £550 - £600.day (Inside IR35) 6 months + Central London/Remote

Salary

£500 - £550 per day

Location

City of London, London

Description

A chance to work in a fast-paced product-focused team implementing forecasting models in order to measure product success

Salary

£70000 - £82000 per annum + Competitive Benefits

Location

London

Description

I'm recruiting for a 12 month FTC leading a team to deliver Lending Strategies and Portfolio Profitability within the Cards space

Salary

£60000 - £65000 per annum

Location

Leeds, West Yorkshire

Description

Digital Network Provider in Leeds - Data Science role!

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.

Mitigating Risk In The Financial Services Sector With Machine Learning

Data & Analytics is an industry that is constantly evolving and is always using the latest technology to innovate its services and capabilities. More recently, these advancements have moved in areas such as Artificial Intelligence (AI) and Machine Learning (ML). Machine Learning is a method of data analysis, under the branch of Artificial Intelligence, that allows systems to learn from data, identify patterns and ultimately make decisions with little to no human intervention. Used across a vast range of sectors, this arm of Data & Analytics has become widely popular, especially within highly-advanced industries such as Finance.  Since the 2008 financial crash, at the top of the agenda for many Financial Institutions (FIs) was, and still is, the need to protect business, increase profitability and, possibly most importantly, address the abundant inadequacies of risk management. This includes risks posed by consumers such as liquidity, insolvency, model and sovereign, as well as any internal process and operational risk the FIs may also be facing through any failures or glitches.   Machine Learning has played a crucial role in improving the quality and precision of FIs risk management abilities. In HPC Wire, it has been reported that the use of AI and ML within the financial sphere to mitigate risk, improve insights and develop new offerings may generate more than $250 billion for the banking industry.  How does ML work? By using incredibly large data sets, drawn (with consent) from consumers, ML can learn, and predict, patterns in consumer behaviour. This can be done in one of two ways: through supervised learning tools, or unsupervised learning tools.  Explained by Aziz and Dowling; “In supervised learning you have input data that you wish to test to determine an output. In unsupervised learning, you only have input data and wish to learn more about the structure of the data.” How do banks use ML to mitigate risk? In FIs, a mix of the two ML tools are used. Most commonly, we can expect to see learning systems such as data mining, neural networks and business rules management systems in play across a lot of banks. These models work in tandem to identify relationships between the data given from the FIs and their consumers – from their profiles to their spending habits, credit card applications to recorded phone calls – which then build ‘character profiles’ of each individual customer. The process can then begin, spotting signs of potential risk factors. This may include debt, fraud and/or money laundering.  Here we break down two key examples.  Fraud Thanks to ML, customers have become accustomed to incredibly quick and effective notification of fraudulent activity from their banks. This ability from FIs comes from large and historical datasets of credit card transactions and machines which have been algorithmically trained to understand and spot problematic activity. As stated by Bart van Liebergen; “The historical transaction datasets showcase a wide variety of pre-determined features of fraud, which distinguish normal card usage from fraudulent card usage, ranging from features from transactions, the card holder, or from transaction history.”  For example, if your usual ‘character profile’ is known by ML tools to spend between £500 - £1000 per month on your credit card, and suddenly this limit is overtly exceeded, fraudulent activity tags will be alerted, and the freezing of your account can be done in real-time.  Credit applications When borrowing from a bank or any other FI, consumers must undertake a credit risk assessment to ensure that they have a record of paying back debt on time, and therefore not adding greater cost, and risk, to the lender.  Traditionally, FIs have approached credit risk with linear, logit and probit regressions but, serious flaws were found in these methods, with many applications being left incomplete. In this space, the evidence for the effectiveness of ML is overwhelming. Khandani et al. found that FIs using ML to analyse and review credit risk can lead to a 25 per cent cost saving for the FI involved.   These ML models come in various shapes and sizes, with the most common being instantaneous apps or websites which allow users and their banks to have access to real-time scoring, data visualisation tools and business intelligence tools.  The risk of risk management with ML Like with any AI or ML application or tool, there will always be cause for concern and real need to always remain vigilant. While ML has shown to be an invaluable tool across lower risk areas, the complexity of more statistical areas of banking, such as loans, has proven to be an Achilles heel for the technology. This usually stems from bias, a perpetual problem for AI and ML across all industries.  Technology Review notes that “There are two main ways that bias shows up in training data: either the data you collect is unrepresentative of reality, or it reflects existing prejudices.”  Data, analytics, AI and ML are notoriously non-diverse working sectors. The person behind the screen creating learning algorithms tends to be white and male, and very unrepresentative of the whole society that the machine learning tool will serve. Over the years, we have heard numerous accounts of unfair and unjust machines, which have learned from a very narrow and unrepresentative dataset, which stems from the lack of diversity amongst the employees within the Data & Analytics industry. For example, Microsoft’s racist bot and Amazon’s sexist recruitment tool, both clear examples that ML and AI are not ready to be used on their own, and humans still need to play an integral part in decision making.  Banks and FIs must be aware of the, potentially lethal, consequences that bias in ML may present. Lenders must be careful to ensure they are working within the guidelines of fair lending laws and that no one group of people are being penalised for no reason other than issues within the technology and its algorithms. It is vital that the humans behind the technology don’t rely on ML to provide them with an answer 100 per cent of the time but, instead, use it to aid them in their decision making when it comes to risk mitigation.  If you’re looking for a role in Data & Analytics or are interested in finance or Risk Analytics, 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.  

What’s Keeping Women Out Of Data Science?

Data Science, the extraction of data to provide meaningful knowledge and insight, is experiencing a surge in growth within Data & Analytics. It is a fast-growing specialism, and talent in this area is in demand, with there being a 650 per cent increase in data science jobs since 2012. Simply put, pretty soon Data Science is going to play a fundamental role in every industry across the globe. Organisations have to adapt and make use of a range of Data Science tools and techniques or they will simply be forced out of business. LinkedIn recognised in their Emerging Jobs report that the role of a Data Scientist sits in the top three in the US, citing significant advancements in the emphasis on using data for this growth. Comparatively in the UK, this role lands within the top 10 at number seven.  Yet, our research tells us that in the UK, 25 per cent of female professionals work within Data Science, with this number dipping to just 20 per cent in the US. So, how can we support more women to enter the specialism? Encourage access to opportunities  Organisations need to continue to hire highly skilled technical talent to keep up with the growth that we are witnessing in the Data Science specialism. Yet, time and time again, working in Data Science can be seen to be an unattractive career proposition – in particular to women. To counteract this, business leaders need to make the role and rewards of becoming a Data Scientist visible within their organisation. Showcasing the range of projects and campaigns that are available, as well as providing opportunities for women to accelerate their careers and follow a pathway that suits them is critical. Education of STEM roles from a young age In order to see more women moving into roles within Data Science, industry leaders from within STEM fields need to take control and lead the way in educating women on the array of opportunities available. Through supporting, organising or hosting workshops, webinars and conferences, organisations can introduce women at entry-level to what careers in Data Science actually look like. This week for example in the UK, we’re currently in the middle of British Science Week. It is initiatives like these that build upon the education that is needed to promote roles in technical fields. Building up communities In the past year, we’ve all come to rely on our connections to provide insight and support during this period of uncertainty and change. This should be a continued focus moving forwards, building communities, networking and sharing knowledge in order to create an informed, educated and engaged workforce that attracts (and retains) female professionals. Within female-focused networks and groups, organisations can support women in advancing their careers, advocating for themselves and acting as a platform to showcase the opportunities that are available to women looking to move into a role in Data & Analytics. The consequence of ignoring these actions is a lack of diversity. We know that diverse teams perform better, and so welcoming in and making the Data Science specialism an attractive career consideration for women is critical. As the industry continues to advance and demand for skilled professionals grows, there will be plenty of opportunity for top talent to make their mark. If you're looking to take the next step in your career or build out a diverse Data & Analytics 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. 

Recently Viewed jobs