Senior Fullstack Engineer - Digital Lending - Quantitative Modelling

OakNorth
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Full-Stack Data Engineer (Python/Java)

Senior Full Stack Data Engineer (Client Facing)

Senior Full Stack Data Engineer (Client Facing)

Senior Data Engineer

Data Engineer

Full Stack Data Engineer (Client Facing)

Join OakNorth as a Senior Fullstack Engineer - Generative AI.

OakNorth is a profitable business that has supported the growth of thousands of entrepreneurs with a new standard of business lending. We're looking for a Senior Fullstack Engineer to join our engineering team in building upon this success, as we grow to play a key part in redefining the future of business banking with next generation financial tools and products.

The role

  • Have a strong technical background, including 5+ years of experience designing and engineering large scale systems
  • Measure your success in terms of business impact, not lines of code
  • Internalise the best ideas from across the organisation, humbly setting a vision that others can get behind
  • Embrace DevOps culture: You build it, you run it
  • Work well cross-functionally and earn trust from co-workers at all levels
  • Care deeply about mentorship and growing your colleagues
  • Prefer simple solutions and designs over complex ones
  • Enjoy working with a diverse group of people with different areas of expertise.
  • You challenge the existing approach when you see the cliff edge racing towards us, but also get on board once the options have been debated and the team has made a decision
  • You're comfortably organised amongst chaos
  • You’re a broad thinker and have the capability to see the potential impact of decisions across the wider business
  • Utilise GenerativeAI: Leverage GenAI tools to increase productivity and enhance decision making processes within the role

Nice-to-Have Skills

  • Experience with AI/LLM integrations and Generative AI applications
  • Knowledge of intelligent document processing and NLP techniques
  • Familiarity with vector databases (e.g., Pinecone, Weaviate) and search platforms (e.g., OpenSearch, Elasticsearch)
  • Exposure to agentic workflows or orchestration frameworks for multi-step AI reasoning, and familiarity with MCP (Model Context Protocol)

Technology

  • We're pragmatic about our technology choices. These are some of the things we use at the moment:
  • Python
  • PostgreSQL, BigQuery, MySQL
  • TypeScript, React, styled-components
  • Jest, React Testing Library, Cypress, pytest
  • AWS, GCP
  • ECS Fargate, Docker, Terraform, GitHub Actions

How We Expect You To Work

  • We expect you to work in these ways, as well as encouraging and enabling these practices from others:
  • Collaborate - We work in cross-functional, mission driven, autonomous squads that gel over time. We pair program to work better through shared experience and knowledge.
  • Focus on outcomes over outputs - Solving a problem for users that translates to business results is our goal. Measurements focused on that goal help us to understand if we are succeeding.
  • Practice continuous improvement - We optimise for feedback now, rather than presume what might be needed in the future and introduce complexity before it will be used. This means we learn faster. We share learnings in blame-free formats, so that we do not repeat things that have failed, but still have confidence to innovate.
  • Seek to understand our users - We constantly seek understanding from data and conversations to better serve our users' needs, taking an active part in research to hear from them directly and regularly.
  • Embrace and enable continuous deployment - Seamless delivery of changes into an environment - without manual intervention - is essential for us to ensure that we are highly productive; consider resiliency; and practice security by design.
  • Test outside-in, test first - TDD keeps us confident in moving fast, and deploying regularly. We want to solve user problems, and so we test with that mindset - writing scenarios first, then considering our solution; coupling tests to behaviour, rather than implementation.
  • You build it, you run it - We embrace DevOps culture and end-to-end ownership of products and features. Every engineer, regardless of their role, has the opportunity to lead delivery of features from start to finish.
  • Be cloud native - We leverage automation and hosted services to deliver resilient, secure services quickly and consistently. Where SaaS tools help us achieve more productivity and better quality results for a cheap price, we use these to automate low value tasks.

How We Expect You To Behave

  • We embrace difference and know that when we can be ourselves at work, we are happier, more motivated and creative. We want to be able to bring our whole selves to work, have our own perspectives and know that we belong. As such, through your behaviours at work, we expect you to reflect and actively sustain a healthy engineering environment that looks like this:
  • A wide range of voices heard to the benefit of all
  • Teams that are clearly happy, engaged, and laugh together
  • Perceivable safety to have an opinion or ask a question
  • No egos - people listen to and learn from others at all levels, with strong opinions held loosely

What Makes Working Here Better

  • This is a truly hybrid role, offering engineers the flexibility to work from home while also providing opportunities to collaborate in person with the team when it adds value. There’s no fixed requirement for days in the office, instead, we focus on creating space for engineers to engage meaningfully, whether that’s in-person for whiteboarding sessions or remote for deep focus work.
  • Work-life balance - 25 days holiday (plus bank holidays) each year, and enhanced family leave allowances.
  • Competitive salary & equity - We want people to have a serious stake in the business.
  • Good kit - Your choice of the best laptop, running macOS or Ubuntu.
  • Team socials - The opportunity to get to know each other outside of work.
  • Company socials - A chance to catch up and meet new colleagues weekly over informal office breakfasts and dinners on OakNorth - or at our free barista bar every day.
  • Commuter support - We offer the cycle to work & EV scheme.

About Us

OakNorth Bank and we embolden entrepreneurs to realise their ambitions, understand their markets, and apply data intelligence to everyday decisions to scale successfully at pace.

Banking should be barrier-free. It’s a belief at our very core, inspired by our entrepreneurial spirit, driven by the unmet financial needs of millions, and delivered by our data-driven tools.

For more information regarding our Privacy Policy and practices, please visit: https://oaknorth.co.uk/legal/privacy-notice/employees-and-visitors/


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.