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Senior Data Scientist (Credit/ Lending)

London
£45000 - £78000 per annum + Shares, culture
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Senior Data Scientist - Credit/ Scorecards

London, UK

Up to £78,000 salary

Financial Services company needs a Data Scientist to join their credit/ scorecard modelling team! If your a Senior Data Scientist with proven experience in Scorecard modelling then Apply Now!

OVERVIEW

This Financial Services provider need a Senior Data Scientist with strong academics and scorecard/ credit modelling experience to join their credit function. Your responsibilities will include but not be limited to:

  • Building scorecard models for consumers using Machine Learning and Decision Science
  • Apply Machine Learning and Decision Science techniques to big financial data-sets to build conclusions on consumers
  • Apply classical ML techniques such as regression and random forests to develop both behavioural and application scorecard models
  • Build insights on customer-centric lending strategies with application of scorecard models

YOUR SKILLS AND EXPERIENCE

  • Minimum of BSc in relatable field (numerate topic) from good university
  • Strong Python and SQL
  • Extensive experience in building scorecard models
  • Experience in classical Machine Learning i.e. regression, random forests etc

THE BENEFITS

  • Exceptional working culture and work/life balance
  • Up to £78,000 salary
  • Strong Bonus

HOW TO APPLY

Please register your interest by sending your CV to Luke Frost via the Apply Link on this page.

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53741
London
£45000 - £78000 per annum + Shares, culture
  1. Permanent
  2. Data science

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Three Ways Data Impacts The Customer Experience

In 2019, over 50 per cent of companies had adopted Big Data, with a further 38 per cent citing that they would be investing in it in the future. As it stands, we can assume that now, at least three-quarters of businesses will have invested in Big Data capabilities. By 2022, the annual revenue from the global big data and business analytics market is expected to reach $274.3 billion.  The lucrative nature of this industry stems from a recognition by many companies that it’s no longer good enough to guess what customers might want or need from your product or service, but to have hard evidence to back up your choices. Not only does this make for much happier, more satisfied customers, but it undoubtedly improves the bottom line.  Here are three examples which showcase how Data can positively impact the customer experience: 1. Create a more intuitive website journey From heatmapping the areas of interest (or disinterest) on your website through eye movement or mouse tracking to traffic analysis through tools such as Google Analytics, Data can give you both real-time and overall information about the success of your website.  You can analyse areas of the website where consumers ‘linger’ or click through, such as content pieces, links or assets, which proves to give added value or entice them to learn more about your business. You can also see areas where little to no activity happens, allowing you to create a new, perhaps more engaging, strategy.  The use of data for website ensures your get the design and content right in less time. The cost of redesigning a website can be a hefty cost for any business. The fewer times a website needs chopping and changing, the more cost-effective it will be, not forgetting to mention a much smoother and more efficient process for customers. 2. Building loyalty through personalisation In a report featured in Forbes by The Harris Poll, 76 per cent of Americans are more likely to complete a purchase if the customer journey has been personalised to them, their needs and wants. The story is similar in the UK, 80 per cent of companies report seeing an uplift after employing personalisation tactics.  However, personalisation must go one step further than just addressing a person by name in an email nowadays. It means targeting consumers with specific and relevant ads that actually take their interest instead of bombarding them with a scattergun approach, as well as looking at areas such as location-specific targeting and device optimised outreach. This can be made possible by combining marketing data, such as brand interactions, combined with sales data, previous purchases, and customer service data, the feedback given. These aspects allow you to create an in-depth and meaningful customer journey map, help you understand what turns specific consumers on, or off, and ensures your marketing messages and outreach are pertinent.  3. Be prepared for problems before they occur Data can give incredible insight into what’s working currently for a business but, arguably, its strengths lie in giving accurate understanding into the potential risks or problems that are likely to occur in the future.  According to Clarion Tech, there are seven areas in which Data can play a crucial role in minimising risk, errors or issues for a vast range of businesses. From making sense of unused business data to making companies proactive instead of reactive, minimising misleading forecasts to diminishing customer service challenges, data can be the solution to a wealth of problems.  Not only do these kinds of errors leave a bitter taste in the mouths of customers who may struggle to revisit your business after a bad experience, but they can negatively affect your bottom line too. Nipping them in the bud before they happen is an incredible card to have to hand, and one that could be the saviour of your business.  To learn more about how working with a Data & Analytics specialist could help bolster the success of your business, contact our team or, if you're looking for your next opportunity, check out our latest roles. 

Charlotte York

Charlotte York

03. June 2021

News

Data Analytics vs. Data Science: Which Should You Pursue?

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 Analyst These 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 Scientist Data 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 Analysts Businesses, 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 Scientists Data 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.  Written by Jena Burner for harnham.com

Guest Blog

Guest Blog

28. May 2021

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