Company Spotlight
Starling Bank AI Jobs & Careers
Starling Bank is the UK's first profitable digital bank, built entirely on modern cloud infrastructure with AI and automation at its core. Unlike Monzo and Revolut, Starling also offers Banking-as-a-Service (BaaS), powering the banking infrastructure for other fintechs — adding an additional layer of ML complexity to their systems.
Editorial content — ObiTech Jobs is not affiliated with Starling Bank.
What AI Is Starling Building?
Starling Bank's AI work spans three distinct contexts: retail banking (3M+ personal customers), business banking (600,000+ SME customers), and Banking-as-a-Service (powering fintech customers like Raisin, Tide, and others through their Engine platform). Each context has distinct ML requirements — personal banking needs consumer-facing fraud and credit models; SME banking needs business credit risk and cashflow prediction models; BaaS needs highly configurable risk engines that can serve different client risk appetites.
Fraud detection — Starling's fraud systems process personal and business transactions. Business banking fraud has different characteristics to consumer fraud — invoice fraud, authorised push payment (APP) scams targeting SMEs, and account takeover have different signatures than personal transaction fraud.
SME credit risk — Starling's business banking growth has been driven by their ability to offer credit products (overdrafts, loans, invoice financing) to SMEs that traditional banks under-serve. ML models that draw on business transaction account data can assess creditworthiness of businesses that lack the credit history that consumer credit bureaus track.
Engine (Banking-as-a-Service) ML — The Engine platform that Starling offers to fintech clients requires highly configurable ML components — risk parameters that each BaaS client can tune for their specific customer base. This is a technically interesting multi-tenancy problem in ML.
Roles Starling Typically Hires For
Data Scientist (Fraud)
Building fraud models for personal and business accounts. Python, tree-based models, time-series features, A/B testing of model changes.
Data Scientist (Credit)
Credit risk models for SME and personal lending. IFRS 9, scorecards, logistic regression, behavioural features from account data.
ML Engineer
Productionising ML models, building feature pipelines, model deployment and monitoring infrastructure.
Data Engineer
Building data infrastructure for Starling's analytics and ML platform — Snowflake, dbt, Kafka, real-time and batch pipelines.
AI Engineer (Productivity)
LLM applications for internal use — developer tooling, customer support assistance, operational AI tools.
Culture & Interview Process
Starling has a distinctive culture — built by founder Anne Boden, who came from the traditional banking world, it retains more structure and process discipline than some challenger peers, while still operating at fintech speed. This is appealing for engineers who want the technical challenge of a fintech without the chaos of an early-stage startup.
The interview process for data and ML roles typically involves a take-home technical assessment, a technical interview, and a values interview. Starling places emphasis on practical data analysis skills and the ability to communicate findings clearly to non-technical stakeholders.
Starling is headquartered in London but has offices in Southampton, Cardiff, and Dublin. They have good hybrid working policies and actively hire outside London.
Quick Facts
2014
London, UK
UK's first profitable digital bank
Fraud, SME credit, BaaS ML
Banking-as-a-Service (Engine)