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Salary Finance

Data Scientist

Posted Yesterday
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In-Office
London, Greater London, England, GBR
Junior
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In-Office
London, Greater London, England, GBR
Junior
As a Data Scientist, you will manage ML solutions lifecycle, focusing on lending decisions, and develop predictive models while collaborating across teams.
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About Salary Finance

Working with employers, we provide a financial wellbeing platform as an employee benefit, helping employees to understand their money better, get out of debt faster and save for their future. We already have a reach of over 4,000,000 employees through our relationships with over 600 of the biggest companies in the UK. 

By improving employee financial wellbeing, we have a very real and meaningful impact on people’s lives. We remove the stress and worry associated with financial difficulties by dramatically reducing the interest rates employees pay on their personal debt, and provide them with the tools needed to start saving sooner and be more financially secure. We are backed by some of the biggest brands, including investments from Blenheim Chalcot (the UK’s leading venture builder), Legal and General (the FTSE 100 insurer and asset manager), Experian and Goldman Sachs, and funding partnerships with JP Morgan and Virgin Money. 

Launched in 2015, we have made excellent progress, and are scaling fast. We are named BITC’s Responsible Business of Year 2018, included in KPMG’s Global Fintech 100, listed top of the Forbes' list of socially-responsible startups, and profiled by the Financial Times, the Times, the Wall Street Journal, the Guardian, the Telegraph, CityAM and the Institute of Directors.

Your role in our mission

You'll own the full lifecycle of ML solutions that drive lending decisions at Salary Finance, where our employer partnerships reach millions of employees across the UK. In your first six months, you'll have shipped at least one scorecard into production.

In 2026, you will focus on our core lending product. - building new scorecards with Credit Risk, developing collections models, and improving the economics that let us lend responsibly at scale. From 2027, the scope widens: marketing models, predictive analytics across our growing product set, and defining new ways to measure customer financial wellbeing.

You'll join a small Data Science team reporting to a Senior Data Scientist. There's real ownership here - you'll define success metrics for your own models and see whether they moved the number.

What you’ll do

  • ML engineering: Design, develop, and deploy end-to-end machine learning pipelines to solve complex business problems, particularly within Credit Risk and Collections.
  • Statistical analysis: Apply rigorous statistical methods to generate actionable insight, not just reports.
  • Model governance: Own model validation, monitoring, and documentation to the standard a regulated lender requires.
  • Cross-functional collaboration: Partner with Marketing, Product, Engineering and Commercial teams to build predictive models that identify opportunities and improve product uptake.
  • Workflow innovation: Apply LLMs and generative AI to automate workflows
  • Communication: Translate complex technical results into clear recommendations for technical and non-technical stakeholders.

Our current stack includes Python, SQL and Git with Linux, Dagster and dbt infrastructure - with active investment in modern MLOps tooling built in Microsoft Fabric.

About you

Essential:

  • Experience: You have 2+ years of professional experience in Data Science or a quantitative analytical role. Prior experience within Financial Services or Fintech is a strong plus.
  • Machine learning modelling: You have built, validated, and monitored predictive models (regression, classification, or time-series) in Python, ideally in a regulated context
  • Python proficiency: You write clean, modular, and testable code capable of running in production environments (not just notebooks)
  • SQL experience: You are comfortable navigating complex relational databases to extract, load and transform data.
  • Outcome-oriented: You define success metrics for your own work and link results back to business value and customer outcomes.
  • Engineering discipline: Version control (Git), documentation, and peer review are habits, not afterthoughts.

Nice to have:

  • Scale-up experience: Experience thriving in fast-paced, high-growth environments where adaptability is key.
  • MLOps: Familiarity with orchestration tools (e.g., Dagster, Airflow), containerization (Docker) and CI/CD pipelines.
  • System architecture: Familiarity with Linux systems and cloud architecture
  • Gen AI: Practical experience with LLMs and ideas for how they can solve real problems in financial services.

Who you are

We embrace our differences but there’s one thing we like to share - our values, so it’s important to us that you are:

  • Fearless, and able to make the impossible possible.
  • Responsible, and want to help build a business that delivers a meaningful difference to society.
  • Dedicated and want to commit to an exciting journey even through the highs and lows.
  • Empathetic and truly care about every colleague and customer.
  • United, because you understand we achieve more when we work as a team.
  • Humble, and take feedback as a way to continuously improve.

What do you get for all your hard work?

  • Company bonus scheme 💰
  • 25 days holiday increasing by one day for every year of service up to 30 days, plus a day off for your birthday 🎂 
  • Hybrid working arrangements so you can work from the office and from home with a budget to help you get set up 🏠
  • Generous company benefits to include pension and life assurance and an annual allowance to spend on medical insurance, health cash plan, denplan, gym memberships 🤸
  • Enhanced policies that are family and pet friendly, to include company sick pay and peternity leave 🐶
  • Great career development in a fast paced environment 🚀
  • Regular company socials
  • Volunteer days as part of our CSR program 🤝
  • More great perks to include weekly snacks, tuckshop, cycle to work, help to save and much more! 🍭

The typical interview process

  • Introductory call with our Talent Manager (phone call - 20 mins)
  • Past experience interview with Hiring Manager (video call - 30 mins) 
  • Technical and culture interview with Team and  Stakeholder(s) (in person -  2 hours)

We’re looking for people that will get stuck in and make a difference. We have a great collaborative, entrepreneurial team that are passionate about what they do. If you want to join a team that is changing people’s lives for the better then we’d love to hear from you.

Learn more at salaryfinance.com

Salary Finance is proud to be an equal opportunities employer. We celebrate diversity and are committed to creating an inclusive work environment where all employees and applicants can flourish.

Top Skills

Dagster
Dbt
Git
Linux
Microsoft Fabric
Python
SQL

Salary Finance London, England Office

58 Wood Lane, London,, London, United Kingdom, W12 7RZ

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