Responsibilities
- Lead four engineers working on the modelling platform and surrounding systems; help the team deliver high-quality solutions.
- Raise engineering standards across modelling systems and infrastructure, with a focus on reliability, observability, reproducibility, and safe change.
- Set technical direction by understanding adjacent systems, workflows, and constraints to identify the right problems to solve at the right time.
- Stay hands-on in the codebase, especially when the team is dealing with ambiguity, cross-system complexity, or problems without an obvious owner.
- Lead by example in how problems are approached and how rigour is balanced with speed of delivery.
- Contribute directly to the systems that matter most, making pragmatic changes that reduce friction, strengthen the platform, and improve reliability in production.
- Engineering decisions in this area directly affect the accuracy of our credit decisions and the volume of lending iwoca can responsibly support.
- Better infrastructure leads to faster experimentation and better models
- Help the team to work faster and with more confidence in the platform. Work with data scientists and strategy analysts to maintain the feedback loop between analysis, strategy and engineering.
Requirements
- A background in a highly numerical discipline – mathematics, physics, statistics, or similar – with an interest in ML and modelling, whether from professional experience or independent work.
- A generalist mindset, with the flexibility and confidence to understand broad technical contexts, challenge assumptions, and suggest a better approach.
- Experience leading engineering teams, combining technical leadership, line management, and individual contribution to deliver cross-functional projects.
- Confidence working in a team where the primary output is statistical models – though you'll own the engineering that supports that work, not the models themselves.
- Advanced proficiency in Python or another object-oriented language.
- A commitment to using modern tools effectively – including AI – to maximise quality, speed, and rigour, while retaining responsibility for accuracy and outcomes.
Nice to Have
- Experience designing and operating ML systems in production, including model serving, monitoring, and retraining pipelines.
- Experience with ML-specific engineering constraints, including data immutability, temporal consistency, and feature store design.
- Experience with experiment tracking platforms, model registries, or MLflow-style tooling.
- Familiarity with Snowflake, PostgreSQL, or similar data platforms used in ML workflows.
- Exposure to probabilistic modelling, Bayesian methods, or statistical inference in a production context.
- Experience with event-sourced data models or time series data pipelines.
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.
Work Arrangement
Hybrid
Team
Team size: 15
Additional Information
- A learning and development budget for everyone.
- Company-wide talks with internal and external speakers.
- Access to learning platforms like Treehouse.
