Data Scientist (Partner Flow Team)

Requirements

• Statistical foundations. Strong grounding in probability and statistics, with an understanding of Bayesian reasoning and experience applying hierarchical modelling or decision-making under uncertainty
• Causal inference. Experience separating signal from noise in observational and experimental data
• Business translation. Ability to understand commercial context and translate data into actionable insights that guide decisions – not just produce analysis, but land it with people who act on it
• Data tooling. Proficiency with data manipulation and modelling tools
• Analytical ownership. Comfortable owning ambiguous problems end-to-end – from framing through modelling to influencing decisions
• AI fluency. Experience with and a passion for using AI to accelerate and automate analysis and model building
• Communication. You write and speak clearly, adapting technical detail to different audiences
• (Desirable) Modelling depth. Experience building or adapting statistical or ML models, with an interest in understanding what’s happening underneath
• (Desirable) Stochastic methods. Familiarity with stochastic modelling concepts as they apply to inference, time series, or uncertainty
• (Desirable) Funnel and marketplace experience. Experience with conversion funnel analysis or marketplace dynamics where you’re competing for attention alongside competitors
• (Desirable) Python. You work primarily in Python. If your background is R or another language, that’s fine – but Python is what the team uses day to day
• (Desirable) Market curiosity. You want to understand how the partner lending market actually works – what makes a partner choose iwoca over a competitor, and what makes a customer convert

What the job involves

• You’ll design experiments, model customer behaviour and analyse large data sets across the partner funnel, and turn findings into product and strategy decisions
• The analytical approach is still being shaped – you’ll influence how it develops, not just what it produces
• The work has direct commercial consequences – on partner platforms, iwoca competes for attention alongside other lenders, and what this team builds and tests affects which customers convert and on what terms
• You’ll work closely with commercial, strategy, and product colleagues across the business
• Experimentation and causal inference. You’ll design and analyse experiments where careful randomisation and interpretation are critical. You’ll need to know where standard A/B testing breaks down and what to reach for instead. The problems are consequential: measuring how pre‑qualification affects Customer Lifetime Value (CLTV), and understanding how different products interact when offered to the same customer base
• Funnel and competitive strategy. You’ll analyse the full partner‑referred customer journey – from application through to credit assessment – identifying where conversion breaks down and why. On partner platforms, iwoca is positioned alongside competitors, so you’ll also figure out what makes customers choose iwoca over the alternatives, and how to shift those dynamics in our favour
• Commercial translation. You’ll be directly involved when decisions get made – not handing off analysis to be interpreted by someone else. You’ll work with commercial and strategy colleagues and coordinate with adjacent teams to make sure the tests and policies you run fit together with their ongoing work
• AI and ways of working. You’ll use AI to accelerate and automate your analytical work. The team treats this as a lever for increasing its rate of learning. You’ll be expected to have a view on where it helps and where it doesn’t
• The team:
• Partner Flow owns the customer journey from the moment someone starts an application on a partner platform through to a completed credit assessment
• By making the application process as smooth as possible and collecting the data needed for a credit decision efficiently, the team maximises the number of partner‑referred customers that receive an offer
• The team works across the full application stack – from the APIs partners use to integrate iwoca, through to the experience customers see when they apply
• The team is small and cross‑functional, combining data science, engineering, design, and product. Active projects include:
• Pre‑qualification – testing how showing customers an offer before they apply affects conversion and long‑term value
• Credit card strategy – iwoca recently launched a credit card alongside its flexi‑loan; the team is working out which product, or combination, to offer which customers
• Funnel optimisation – identifying and reducing the biggest points of friction across the partner journey

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