Data Scientist - Credit Risk Modelling

United States Digital Space LLC

Greater London

Hybrid

GBP 60,000 - 90,000

Full time

11 days ago

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Benefits offered by this job

Flexible working hours

Vitality medical insurance

Private GP service

25 days vacation

Birthday day off

Buy/sell leave option

Sabbatical after 4 years

Counselling access

3% pension contributions

Equity incentive

Parental leave & nursery benefit

Electric car scheme

Cycle to work

Company retreats

Job summary

United States Digital Space LLC in London (hybrid) or remote UK is seeking a Data Scientist to join our Credit Risk Modelling team. You’ll work on probabilistic models that influence lending terms and portfolio strategy.

The role covers production model health, iterative development, and R&D, with opportunities to prototype using AI and push research into production. The team values evidence-based decisions and clear communication.

Qualifications

Statistical foundations from a quantitative field; you reason about uncertainty and calibration as central concerns.
Active research mindset; you explore new ways to add value and expect to push the next step change.
You critically evaluate model output and can defend your reasoning under challenge.
End-to-end ownership of ambiguous problems, moving fast and iterating on new evidence.
You use AI as a primary tool and can judge where it helps or not.
Clear, concise written and verbal communication tailored to your audience.

Responsibilities

Run credit risk and CLtV modelling projects alongside the team.
Keep production models healthy, develop incrementally, and conduct research to reshape models.
Prototype with AI to explore viable approaches and drive R&D share.
Contribute to causal estimation of offer terms and IFRS accounting model work.
Generalise credit and CLtV frameworks to explore broader modelling options.

Skills

Python

Bayesian methods

Time series modelling

Statistical foundations

Research mindset

Judgement

Analytical ownership

AI fluency

Communication

Tools

Python

SQL

Job description

Data Scientist - Credit Risk ModellingHybrid in London or remote in the UK

We’re looking for a Data Scientist to join our Credit Risk Modelling team.

You’ll work on the credit models that sit at the core of the company's lending business – the models that decide who we lend to, on what terms, and how far the product can grow.

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 the company. 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 the company'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, who combined create models to efficiently drive fully automated and human-in-the-loop decision making. This forms the core analytical team that interface with and support smaller analytic functions in other parts of the business.

The role

You’ll run credit and CLtV modelling projects alongside the rest of the team. 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..

IFRS accounting model. A multi-stage credit model where information propagates back from later-stage recovery predictions to sharpen upfront loss estimates.

Generalising credit and CLtV. Whether a more general framing could replace both separate models is an open research question.

The requirements

Essential:

Statistical foundations. You have a background in probability and statistics from a quantitative field. You reason about uncertainty and calibration as first-order concerns.

Research mindset. You're actively exploring new ways to add value. R&D time is when you expect to find the next step change.

Judgement. You critically evaluate model output – yours, a colleague's, or an LLM's – and can explain why a choice is right. You defend your reasoning under challenge, and challenge others' when the evidence points elsewhere.

Analytical ownership. You take ambiguous problems end to end, from framing to a landed decision. You like to move fast, iterate, and update on new evidence rather than chase perfection.

AI fluency. You use AI as a primary tool. You prototype with it, automate with it, and take on R&D that would not otherwise be viable. You use judgement on where it helps and where it doesn't.

Communication. You write and speak clearly, directly, and concisely. You adapt technical detail to your audience.

Bonus:

Domain experience. You have worked in credit risk, lending, or customer lifetime value modelling.

Production ML. You have built and shipped supervised ML models end to end – exploration, training, deployment, monitoring.

Non-linear methods. You can think in terms of the cost function and inductive biases of your models

Bayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work.

Time series modelling. You have modelled temporal data where autocorrelation, drift, or seasonality mattered.

Python. The stack the team uses.

The salary

We expect to pay from £60,000 – £90,000 for this role. But, we’re open-minded, so definitely include your salary goals with your application. We routinely benchmark salaries against market rates, and run quarterly performance and salary reviews.

The culture

At the company, the best idea wins. We model our culture on independent thinking, challenging untested logic, and evidence-based decisions. We prioritise learning and growth, and give people the autonomy to develop in the direction that makes them most effective.

We’re a tech company and believe in the power of AI to help us work faster and better. We provide the infrastructure where every iwocan always has access to the best models and where those models have access to all of our data. We will help our people to learn how to use and grow with the new tools available to them.

The offices

We put a lot of effort into making the company a great place to work:

Offices in London, Leeds, Berlin, and Frankfurt with plenty of drinks and snacks.

Events and community-led groups, including running groups, padel, and monthly ping-pong and pool competitions.

The benefits

Flexible working hours.

Medical insurance from Vitality, including discounted gym membership.

A private GP service (separate from Vitality) for you, your partner, and your dependents.

25 days’ holiday per year, an extra day off for your birthday, the option to buy or sell an additional five days of annual leave, and unlimited unpaid leave.