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# Data Scientist - Broker Terms

- **Company:** iwoca
- **Location:** London, United Kingdom
- **Salary:** £70,000 - £80,000
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Experimental Data, Machine Learning, NumPy, Scientific Computating, Data Processing, Pandas, Scikit Learn, Modeling and Simulation
- **Published:** October 25, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5941245892

## About the Role

Essential:

 * Statistical foundations. You have a background in probability and statistics from a quantitative field such as mathematics, physics, or statistics. You're comfortable with Bayesian reasoning, and have applied hierarchical models or formal decision-making under uncertainty in real work.
 * Causal inference and experimentation. You've separated signal from noise in observational and experimental data. You know where A/B testing breaks down and what to reach for instead. You have experience with careful experimental design to facilitate clean, trustworthy results.
 * Analytical ownership. You take loosely defined problems from start to finish - framing the question, choosing the method, and landing the answer with the people who act on it.
 * Data tooling. You're proficient with data manipulation and modelling tools, and with scientific computing libraries (pandas, numpy, sklearn, PyMC).
 * AI leverage. You've used AI to produce work you couldn't have delivered without it - an analysis pipeline, a prototype, or a tool others use. You judge where it helps and where it doesn't, and you check its output before anyone acts on it.
 * Communication. You write and speak clearly, directly, and concisely. You adapt technical detail for non-technical colleagues in commercial and strategy roles.

## Description

The team is small and interdisciplinary, combining commercial strategy, engineering, and data science. The work translates business questions into quantitative problems, and findings into actionable decisions. Active projects include:

 * Model-based pricing - creating a sophisticated model to offer bespoke pricing to each customer
 * A no-fee test - examining how our product performs when we remove all drawdown fees, and how that influence customer repayment behaviour
 * Testing longer-duration flexi-loan products with particular customer segments.
 * Reintroductions - exploring the right offer terms to give returning customers

The team is small and cross-functional - commercial strategy, engineering, design, and data science - and works across the full application stack, from the Broker API to the Introducer Portal brokers use.

The role

You'll model expected customer take-up rates, design experiments, and translate findings into pricing and product decisions for the acquisition channel that drives the most customer lifetime value (CLTV). Because the Broker Terms team is a young team, you'll influence how its analytical approach develops, not just what it produces.

The work has direct commercial consequences: brokers act on what the team builds and tests, and that shapes which customers iwoca wins and on what terms.

 * Offer terms and experimentation. You'll model how customers respond to offer terms and how brokers make decisions under uncertainty. You'll design experiments where causal inference is central: you'll need to know where standard A/B testing breaks down and what to reach for instead.
 * Commercial translation. You'll work with commercial and strategy colleagues and see your analysis applied directly in day-to-day decisions. That includes meeting brokers in person - gaining context that shapes how you frame and interpret results - and coordinating with adjacent teams responsible for underwriting decisions, repeat lending, and offer construction.
 * CLTV and customer journey. You'll think across the full broker-referred customer journey - from first funding to repeat - identifying what drives CLTV and what changes to offer terms or UX improve it. You own both the strategy and the mechanism, so your findings feed directly into what the team builds and tests.
 * AI and ways of working. You'll use AI to accelerate and automate your analytical work - not occasionally, but as a default. You should have a view on where it genuinely helps and where a different approach is better., * Modelling depth. You've built or adapted statistical or ML models and can explain the maths behind them to commercial stakeholders.
 * Stochastic methods. You've used stochastic processes for inference or time-series modelling.
 * Broker market insight. You've worked with brokers or other intermediaries in lending or financial services.