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.
DESCRIPTION
The role of the lead data scientist is focused on building and running the data science discipline and delivery mechanisms for data science artefacts internally. The Lead data scientist is a person who generates value by “putting our data to work” through the use intelligent systems. Working with data engineers and data analytics teams to make advanced calculations to derive conclusions. As a lead data scientist you will receive data that has passed a first round of cleaning and modelling, which you then can feed to sophisticated analytics models, machine learning and statistical methods to automate decisions. The lead data scientist will have high levels of autonomy and responsibility for intelligent systems and related artefacts and would suit a candidate that wants to join an existing data science team and help us level up.
RESPONSIBLITIES
- The lead data scientist will have to do the following key functions (Data exploration and visualization, experimentation and prediction):
- Capture the sources of data and analyses them to build the best Intelligent models.
- Key Models to be built as a team:
- LTV, Churn, Portfolio Risk, Creditworthiness/affordability, Behavior Economics
- Design intelligent systems and work with Data Engineers to utilise technologies to convert unstructured data into structured data and embeddings.
- Reviewing Payments and Purchasing Habits across all customer segments.
- 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
- Manage and mentor other data scientists
TYPICAL SKILLS & BACKGROUND
- Key background: Statistics, Econometrics, Computer Science (Will have a STEM degree discipline)
- Proficiency in statistical software packages and functional programming languages (any of the following SQL, SPSS, R, Python, 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 designing and implementing machine learning algorithms tailored to specific business needs and tested on large datasets.
- 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.
- Experience running teams of around 5 including a range of abilities from associate/intern level through to principal level.