Data Scientist - Repeats Team

Hybrid in London, United Kingdom

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 Repeats team is responsible for the lending product strategy and digital user experience of recurring customers. They are responsible for all the product decisions that occur after a customer has taken their first funding. This includes the user journey eligibility and documents requirements and offer terms (such as offered amount or pricing), among other things.

iwoca's Data Scientists specialise in supervised machine learning, statistical inference and exploratory data analysis, focusing on tabular and time-series data. Their work emphasises quantitative predictions through the analysis of conditional probabilities and expectations, using medium-sized datasets.

Your primary focus will be to figure out the best document requirements and offer terms (pricing, amount, duration, additional product features, etc.) to maximise user conversion and value to iwoca. You will look at observational data, identify hypotheses for unknown relationships, figure out their commercial relevance, and design and implement tests to prove or disprove your theory.

• Work closely with the Repeats team lead and tech leads to identify and quantify opportunities within the customer journey, based on past data or modelled assumptions.
• Design experiments to compare the performance of different strategies and evaluate them rigorously to make unbiased decisions.
• Share your findings and modelling with the wider business to impact strategy. iwoca lives and breathes data as a company, so data scientists play an important role in decision-making.

• Own some of our monitoring and data pipelines, covering all aspects of existing customers' user journey, and getting the data we need to make informed decisions.
• Lead the move away from rule-based policies towards machine learning approaches to improve predictive accuracy and optimise decision-making.
• Promote analytical rigour within the team, ensuring that our experimental designs are correctly defined and that we evaluate tests without bias.

• Join our community of analysts/data scientists/statisticians to ensure alignment in methodology across iwoca. You’ll be part of a peer group with whom you can discuss best practices or the latest advances so that you share and deepen your technical expertise.
• Build expertise across the full range of the Repeats funnel, from the user journey to the lending strategy, learning to understand how customers evolve in our complex environment.

• Expertise in probability and statistics, gained through work in a quantitative field such as Mathematics, Statistics, Physics, Engineering, or similar
• Ability to understand business context and translate data into actionable insights that guide decisions
• Proficiency with data manipulation and modelling tools such as pandas, stats models, or R
• Experience with scientific computing tools such as Num Py or Sci Py
• Ability to work autonomously and manage analytical work from framing the question through to making a recommendation
• Ability to communicate clearly in writing and speech, adapting technical detail to different audiences

• Understanding of Bayesian statistics, including interest in or experience with hierarchical models
• Experience building or adapting statistical or machine-learning models,…