Senior Data Scientist - Credit Risk Modelling

United States Digital Space LLC

Greater London

Hybrid

GBP 90,000 - 120,000

Full time

14 days+

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Benefits offered by this job

Flexible working hours

Vitality medical insurance

Private GP service

25 days holiday + birthday

Sabbatical after four years

Counselling & therapy

3% Pension contributions

Equity incentive

Parental leave + nursery scheme

Electric car scheme

Company retreats

Learning budget

Treehouse access

Job summary

United States Digital Space LLC in London or remote in the UK is seeking a Senior Data Scientist to lead credit risk and CLtV modelling on end-to-end projects. You will own production ML pipelines, model health, deployment and monitoring, while driving R&D to improve accuracy and business impact.

The role blends hands-on modelling with direction-setting for the team. You’ll work across fast-moving initiatives with a twelve-person data science team, tackle multi‑quarter projects, and shape the

Qualifications

Background in probability and statistics from a quantitative field
Experience building and shipping supervised ML models end-to-end
Proactive exploration of new ways to add value using R&D time
Ability to critically evaluate model output and defend reasoning
Owned modelling projects end-to-end and driven commercial impact
Experience using AI as a primary tool and applying it judiciously

Responsibilities

Own credit and CLtV modelling projects end to end
Maintain production models, iterative development and monitoring
Lead research to reshape model architecture and methods
Collaborate with cross-functional partners to land business impact

Skills

Statistical foundations

Production ML

Research mindset

Judgement

Project leadership

AI fluency

Communication

Tools

Python

Job description

Senior Data Scientist - Credit Risk ModellingHybrid 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 the company 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 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. 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:

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.
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.
Production ML. You have built and shipped supervised ML models end to end – exploration, training, deployment, monitoring.
Research mindset. You proactively explore 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. You’ve influenced technical direction beyond your own projects.
Project leadership. You’ve owned modelling projects end to end, from spotting the opportunity through framing, method choice, shipping, and landing the commercial impact. You 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.
Non-linear methods. You have shipped gradient boosting or neural networks on tabular data in production.
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 £90,000 – £120,000 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.
A one‑month, fully paid sabbatical after four years.
Instant access to external counselling and therapy sessions for team members that need emotional or mental health support.
3% Pension contributions on total earnings.
An employee equity incentive scheme.
Generous parental leave and a nursery tax benefit scheme to help you save money.
Electric car scheme and cycle to work scheme.
Two company retreats a year: we’ve been to France, Italy, Spain, and further afield.