Nomo Fintech Careers

Who we are

BB2 Digital and Technology Services Ltd (t/a Nomo Fintech) is a cloud-based business-to-business Fintech company which owns the digital platform that powers the digital retail banking services of Bank of London and The Middle East plc (“BLME”), branded Nomo (available on iOS and Android), and provides various other services to BLME.

Nomo Fintech is currently in scale-up mode to support international digital banking across the GCC, and it’s an incredibly exciting time to join the business with great ambition and an effective combination of talent, culture, and world class technology.

Nomo Fintech leverages the support services of an intragroup entity based in Dubai which houses various functions to support Nomo Fintech’s business services.

Role Description

The role of the Data Scientist is focused on interpreting the generated data.  The Data Scientist is a person who helps to make sense of insights that were received from data engineers or business analytics to make advanced calculations to derive conclusions. As a Data Scientist, you will receive data that has passed a first round of cleaning and manipulation, which they can feed to sophisticated analytics programs and machine learning and statistical methods to prepare data for use in predictive and prescriptive modelling. The Data Scientist will utilize the source of data and analyses to create predictive models that can be applied to business.

Responsibilities

• The data scientist will have to do the following key functions (Data exploration and visualisation, experimentation and prediction):
• Capture the sources of data and analyses them to build the best predictive models.
• Market back testing or Live simulations using the Predictive models.
• Design or utilize technologies to convert unstructured data into structure data.
• Reviewing Payments and Purchasing Habits across all customer segments.
• Evaluate a customer’s payment history and purchase history to determine Lifetime Customer Value.
• Determine and clarify the lifetime value of every customer for example using data science within an     organization to power crunch massive amounts of data to build asset management models to earn higher risk adjusted returns for their clients for example
• Utilizing data science to build Fraud Detection and Prevention models based on behaviour.
• Present and explain data to others. They must be able to communicate data to people of different skill sets, explain the importance of patterns in the data, and suggest solutions.
• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
• Mine and analyze data from company databases to drive optimization and improvement of our products, marketing techniques and business strategies.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Create visualisations to communicate insights to management and stakeholders across the company.
• Develop processes and tools to monitor and analyze model performance and data accuracy

• Key background: Statistics, Econometrics, Computer Science (Will have a STEM degree discipline)
• Proficiency in statistical software packages and functional programming languages such as : SQL, Tableau, SAS, Stata, SPSS, R, Python, RapidMiner, Knime, Matlab, Wolfram Mathematica and C++, or Java.
• Will have at least 2-3 years of financial services based, data analytics experience. Preferably experience within the fraud and financial crime domain, or card payments
• Will have experience working with multiple and large unstructured datasets
• Experience in an analytical role involving machine learning techniques, data extraction, analysis, and communication.
• Experience in designing and implementing machine learning algorithms tailored to specific business needs and tested on large dataset.
• Experience in data mining and using databases in a business environment with large-scale, complex datasets.
• Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to research scientists, engineering teams and business audiences.

• Annual Discretionary Individual Performance Bonus up to 30%
• Generous Pension Scheme (up to 20% in total)
• 30 days Annual Leave plus Bank Holidays & an option to buy/sell 5 days
• Private Healthcare and wellbeing incentives, deals and discounts with Vitality
• Dental and Mental Health Allowance
• A hybrid working environment - work from home allowance
• Choice of Tech
• Group Income Protection – Up to 75% of salary in event of long-term sickness.
• Personal Development and progression plans including £1,000 per employee per annum
• Personal Gym Membership Budget
• Annual Health Check Up
• Season ticket loan
• Cycle to work scheme
• Employee Assistance Programme – includes mortgage and pension advice
• Regular social/company events (in person and remotely)