Senior Data Scientist

Funding Circle Ltd.
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

We are looking for a Senior Data Scientist to join us as we continue to evolve and build our next generation of models at Funding Circle. You will play a key role in developing, implementing and monitoring these models and drive significant value for the business.

As part of theDecision Scienceteam, you will create cutting edge statistical and machine learning models and collaborate with data engineers, analysts, and business stakeholders. As well as improving on our previous generation models, we are expanding our borrower products so you will have the opportunity to develop completely new models.

Who are we?

We’re Funding Circle. We back small businesses to succeed.

At Funding Circle, we believe the world needs small businesses. That’s why we’ve made it our mission to help them get the finance they need to grow.

With more than a decade of expertise under our belt, we’ve built a game-changer of a platform with cutting-edge data and technology that’s reshaping the landscape of SME lending. Say goodbye to lengthy applications and hello to lightning-fast decisions! In just minutes, SMEs across the UK can get a decision, giving them access to competitive funding in a flash.

We know that good business is about good people. So we pride ourselves on providing meaningful, human support as well as fast, hassle free processes to deliver an unbeatable customer experience.

The role

  1. Develop statistical and machine learning models for commercial lending products
  2. Deliver value through optimisation of our current models and drive innovation through constantly seeking alternative data sources
  3. Be an excellent communicator and presenter. Turn complex analysis into simply articulated visions / insights.
  4. Have a clear and relentless focus on delivering business results and be comfortable challenging the status quo
  5. See projects right through to completion, by leading on the implementation efforts and developing ongoing monitoring plans
  6. Proactively problem solving, identifying and confidently mitigating any risks, issues or control weaknesses that arise in your day-to-day

Please note, the minimum expectation for office attendance is three days per week in our central London office.

What we’re looking for

  1. At least 4+ years experience in a Decision Science setting, ideally within SME or retail lending environment
  2. Advanced knowledge of statistical and machine learning methods e.g. logistic regression, gradient boosted trees, NLP
  3. Advanced knowledge of Python & SQL
  4. Proven track record of driving changes within an organisation
  5. Exceptional problem solving skills using logical reasoning and analytical methods
  6. Likes to get into the details and solving problems but can also step back and communicate the bigger picture
  7. Builds collaborative and constructive relationships and is passionate about what they do
  8. Cares about our mission to support small businesses

At Funding Circle we are committed to building diverse teams so please apply even if your past experience doesn’t align perfectly with the requirements.

Why join us?

At Funding Circle, we celebrate and support the differences that make you, you. We’re proud to be an equal-opportunity workplace and affirmative-action employer. We truly believe that diversity makes us better.

As a flexible-first employer we offer hybrid working at Funding Circle, and we've long believed in a 'best of both' approach to in-office collaboration and non-office days. We expect our teams to be in our London office three times a week, where you can take advantage of our newly refurbished hybrid working space, barista made coffee and subsidised lunches (via JustEat) every day!

We back our Circlers to build their own incredible career, making a difference to small businesses every day. Our Circler proposition is designed to support employees both in and out of work, and it is anchored around four pillars: Health, Wealth, Development & Lifestyle.

A few highlights:

  • Health:Private Medical Insurance through Aviva, Dental Insurance through Bupa, MediCash, access to free online therapy sessions and exclusive discounts with Hertility for reproductive health support.
  • Wealth:Octopus Money Coach, free mortgage advisor partnership and discounts across numerous retailers through Perks at Work.
  • Development:Dedicated annual learning allowance and full access to internal learning platform.
  • Lifestyle:Wellhub (for fitness discounts), Electric Car Scheme and more!

And finally, we have award winning parental leave policies supporting parents through enhanced maternity, partner and adoption leave, as well as additional leave for parental bereavement and for fertility treatments.

Ready to make a difference? We’d love to hear from you.

#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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.