Let us tell you a bit about the opportunity:
The Modelling team acts as the centre of excellence for all financial modelling and analytics within Aldermore’s Finance & Treasury division. Analytics and data science are core to the Group’s strategy, and this team are at the forefront of implementing these for stakeholders up to board level, as well as championing the culture of “staying curious” and utilising the latest technology.
The team develops, implements, and validates various models that help determine the structure of the balance sheet. At the same time, the team also works on complicated financial models, in collaboration with Financial Controllers, Treasury Markets, Pricing etc.
It is an enjoyable and exciting, ever-changing area to work in, and at the heart of Finance and the wider Group’s business.
What will your day look like?
* Own and contribute to the full model lifecycle for Finance and Treasury models, from model selection and data analysis through build, validation, calibration and ongoing use.
* Develop and maintain financial models using Python, SAS, AI/ML and other appropriate techniques, including prepayment and deposit models.
* Analyse, cleanse and interpret financial data to support both model development and ad-hoc analytical requests.
* Validate and implement models across systems such as QRM, Anaplan and Excel, ensuring robust documentation, testing and governance.
* Act as a key modelling and data subject-matter expert, working closely with Treasury, Finance, Risk and business stakeholders.
* Participate in the Model Management Framework and support wider Finance initiatives, including executive analysis and regulatory processes (e.g. ICAAP, ILAAP).
What do we expect from you?
General experience includes:
* A high-level of expertise with quantitative financial models, in particular Treasury, Balance Sheet or Finance models
* Understanding and experience of the full model life-cycle: Selection, Development, Validation, Calibration, Implementation
* Data analysis experience and ability
* Working within Treasury/Risk/ALM/Analytics for a significant period of time, preferably at a retail bank/building society
Technical Experience includes:
* Coding experience in a statistical language (Python, R, S+, SAS, etc.), preferably R and/or Python
* Implementation of financial and/or behavioural models for ALM, Treasury, Accounting or Finance, preferably within QRM or Anaplan
* Excellent Excel and VBA
* Good understanding of data, statistics and applied financial and behavioural modelling
* Data Science skillset - data-mining, visualisation (eg: Tableau), ML & AI, presenting, coding (SQL, Python, R, etc)
What can you expect from us?
* A friendly and flexible culture, the same as how we work with our customers. Hybrid working model – 2 days a week in the office
* A growing organisation that means there’s lots of opportunities to progress
* A drive for continuous improvement, which you will be empowered to get behind from day one.
* And of course, you will be rewarded competitively, with a good range of core benefits and bonus potential.
Let us tell you a bit more about us
We’re Aldermore – the award-winning bank, trusted and highly rated by over a quarter of a million customers for more than a decade. With our range of specialist mortgages, savings accounts and business finance solutions, we're backing more people to go for it.
We thrive by saying “yes” to our customers. We respect the ingenuity of entrepreneurs and their startups; we give first-timers a leg-up onto the property ladder; we open up the lending market to many; and thousands of customers chose Motonovo Finance every week to buy their next car, van or motorbike.
This is where you come in. We are on a journey. A journey defined by a destination; to deliver on our purpose.
Still curious?
Join us and we’ll make the same promises to you as a colleague, as we do to each of our customers. We’re committed to building a working environment that values respect, diversity, and compassion. We welcome people regardless of age, disability, gender identity, marital status, race, faith or belief, sexual orientation, socioeconomic background, and whether you’re pregnant or on family leave.
Please note that we have a thorough referencing process, which includes criminal record checks.