Senior Data Scientist - Credit Risk Modelling
Senior Data Scientist on iwoca's Credit Risk Modelling team, owning credit risk and customer lifetime value models end to end for the company's UK and German SME lending — spanning production ML, causal research, and multi-quarter technical direction. Python-based, hybrid in London or remote within the UK, paying £90k–£120k.
- Compensation
- £90K–£120K / yr
- Location
- London, United Kingdom
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Clear, direct, concise written and spoken communication adapted to the audience
- Background in probability and statistics from a quantitative field; reasoning about uncertainty and calibration
- Built and shipped supervised ML models end to end (exploration, training, deployment, monitoring)
- Research mindset — proactively exploring new ways to add value
- Judgement: critically evaluates model output and has influenced technical direction beyond own projects
- Project leadership: owned modelling projects end to end, from opportunity to commercial impact
- AI fluency — uses AI as a primary tool for prototyping, automation and R&D
Nice to have
- Domain experience in credit risk, lending, or customer lifetime value modelling
- Shipped gradient boosting or neural networks on tabular data in production
- Used hierarchical models, MCMC, or Bayesian updating in real work
- Time series modelling experience with autocorrelation, drift, or seasonality
- Python (the team's stack)
Full description
Senior Data Scientist - Credit Risk Modelling
Hybrid in London or remote in the UK
We’re looking for a Senior Data Scientist to join our Credit Risk Modelling team.
You'll shape how iwoca models credit risk – setting the technical direction on multi-quarter projects, and lifting the bar for the team as you go.
The company
Small businesses move fast. Opportunities often don’t wait, and cash flow pressures can appear overnight. To keep going, and growing, SMEs need finance that’s as flexible and responsive as they are.
That's why we built iwoca. Our smart technology, data science and five-star customer service ensures business owners can act with the speed, confidence and control they need, exactly when it's needed.
We’ve already cleared the way for 100,000 businesses with more than £4 billion in funding. Our passionate team is driven to help even more SMEs succeed, through access to better finance and other services that make running a business easier. Our ultimate mission is to support one million SMEs in their defining moments, creating lasting impact for the communities and economies they drive.
The team
The Credit Risk Modelling team owns credit risk and Customer Lifetime Value (CLtV) modelling for iwoca's UK and German lending. That covers the probabilistic machine learning models behind every credit decision, plus the CLtV models that shape pricing and portfolio strategy.
The team is around twelve data scientists. Some work on auto-space models that decide within five minutes from data candidates authorise us to pull. Others work on manual-space models that pick up when a credit analyst adds digital footprint and income data, which lands a decision within 24 hours.
The role
You'll own credit and CLtV modelling projects end to end, from spotting where the modelling stack is holding the business back through to landing the change. The work spans keeping production models healthy, incremental development, and research that reshapes how the models work. AI has lowered the cost of prototyping enough that ideas which used to sit below the priority line are now viable, so the R&D share of the role is growing.
Live examples of the work:
- Causal estimation of offer terms. Modelling how amount, duration, and price shape customer outcomes.
- Unifying auto and manual models. The two families were built without forced technical alignment. Finding a principled way to unify them on a common cost function is open work.
- IFRS accounting model. A multi-stage credit model where information propagates back from later-stage recovery predictions to sharpen upfront loss estimates.