Data Scientist - Third-Party Acquisition

Description

Your work will set the direction for growth in Germany through brokers and partners, using experiments and models.

This is a Data Scientist role on the Third-Party Acquisition (3PA) Germany team, hybrid with a base in London.

The company lends to small businesses that need finance able to move as fast as they do. It has helped over 100,000 businesses with more than £4 billion in funding.

The company favours independent thinking, challenges untested logic and makes evidence-based decisions. It backs learning and autonomy, and gives everyone access to the best available AI models and all of its data.

The team has 9 people across commercial strategy, analytics, product and engineering.

3PA Germany brings small businesses to the company through 2 channels and owns each journey from application to take-up. Brokers submit applications for their clients. Partners are platforms such as digital banks and accounting software providers that offer lending to their own customers.

Responsibilities

• Find out what the data shows across both channels. Most of your time goes on experiment design and statistical modelling of tabular data, with some machine learning.
• Turn your findings into recommendations and refine them with commercial and product colleagues.
• Decide which questions to tackle first and how to answer them.
• Shape which businesses the team reaches, how they convert and the terms they receive.
• Join the company's data science community and share methods with data scientists in the UK broker and partner teams.
• Funnel experiments: design and analyse controlled tests across broker and partner funnels, including where clean randomisation is difficult.
• Pre-qualification: build the model that tells partner customers how much they could borrow before applying.
• Take-up and lifetime value: model what leads a business to accept an offer and what raises its lifetime value.
• Competitive position: find out why businesses coming through brokers and partners pick the company over other lenders, and test journey changes that win more of them.
• Offer terms: study how lending product terms affect conversion in the broker channel, and use the results to guide product and risk decisions.

Requirements

• Essential: a background in probability and statistics from a quantitative field such as mathematics, physics or statistics.
• Essential: experience designing and analysing experiments and estimating causal effects from observational data, with knowledge of where A/B testing fails and what to use instead.
• Essential: commercial judgement, so you understand the business context of each question and turn data into recommendations people act on.
• Essential: the ability to take loosely defined problems from framing the question through to a decision.
• Essential: coding for analysis in Python. R is acceptable to begin with.
• Essential: experience using AI to produce work you could not otherwise have delivered, such as a pipeline, prototype or tool others use. You judge where it helps and check its output before anyone acts on it.
• Essential: clear, direct and concise writing and speaking, with the ability to explain technical detail to non-technical commercial and product colleagues.
• Bonus: Bayesian statistics, with hierarchical models applied or a strong wish to learn them.
• Bonus: building or adapting machine learning models, with curiosity about the maths behind them.
• Bonus: knowledge of how lenders price loans or assess credit risk.

Conditions

Pay is £70,000 to £90,000.

Include your salary goals with your application. Salaries are benchmarked against market rates, with quarterly performance and salary reviews.

Hybrid working based in London.

Flexible working hours.

Benefits

• 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 a year, an extra day off for your birthday, the option to buy or sell 5 days of annual leave, and unlimited unpaid leave.
• A fully paid 1-month sabbatical after 4 years.
• Immediate access to external counselling and therapy for 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.
• 2 company retreats a year, previously held in France, Italy, Spain and further afield.
• A learning and development budget for everyone, company-wide talks with internal and external speakers, and access to learning platforms such as Treehouse.
• Drinks and snacks in the London, Leeds, Berlin and Frankfurt offices, plus events and community groups including running, padel, and monthly ping-pong and pool competitions.