Data Scientist in London

Data Scientist in London

London Full-Time 42000 - 52000 £ / year (est.) Home office (partial)
Social Finance

At a Glance

  • Tasks: Build data pipelines, generate insights, and support live systems in a collaborative environment.
  • Company: Join Social Finance, a not-for-profit tackling social issues with innovative solutions.
  • Benefits: Competitive salary, flexible working options, and opportunities for training and career progression.
  • Other info: Be part of a mission-driven culture that values curiosity and empathy.
  • Why this job: Make a real impact on social challenges while developing your data science skills.
  • Qualifications: Experience with Python, data analysis, and a passion for solving real-world problems.

The predicted salary is between 42000 - 52000 £ per year.

This hands‑on, production‑focused role involves contributing to the full lifecycle of data science—building data pipelines, generating insights, and supporting live systems. You will work alongside experienced data scientists, software engineers, and product managers, and receive structured support as you grow confidence in production environments.

In the first year, you will develop and maintain data pipelines, support existing digital products, and contribute to short‑term projects that provide data‑focused support to non‑technical teams. Over time, you will take increasing ownership of data‑science backlogs, deploy and manage changes safely, and continue collaborating across the team. This role suits someone who enjoys solving real‑world problems with data, is comfortable with a range of technical tasks, and is interested in how emerging AI tools can transform data science.

Join a small multidisciplinary team that bridges data science, data engineering, and software development. The team collaborates with product managers and stakeholders across the public sector (NHS, central government, voluntary sector). Modern development practices—version control, code review, CI/CD—ensure robust, tested, and safe production deployments. As an early‑career member you will receive ongoing support while working on real operational systems. Given the team’s size, you will have opportunities to contribute to new techniques and rapidly evolving AI developments.

Key Responsibilities
  • Build and extend data pipelines using Python following established templates and patterns.
  • Contribute to ingestion, transformation, and validation of data within a shared platform.
  • Support ongoing improvement of pipeline performance, reliability, and scalability.
  • Participate in a collaborative development process using GitHub, including code reviews and version control.
  • Write clear, maintainable, tested, and well‑documented Python code suitable for production.
  • Support testing and deployment within established CI/CD processes.
  • Assist short‑term data projects, helping non‑technical teams integrate data into solutions.
  • Clean, analyse, and interpret data to generate insights for stakeholders.
  • Develop dashboards and visualisations (e.g., Power BI) to communicate findings clearly.
  • Respond to client user requests, such as queries about data outputs.
  • Collaborate with stakeholders to understand requirements and improve existing products.
  • Stay informed about data science and AI developments and explore opportunities for improvement.
About You
  • Experience with Python for data analysis or processing, including libraries such as pandas or NumPy.
  • Understanding of data‑analysis workflows—cleaning, transformation, exploratory analysis.
  • Motivation to develop skills with production data systems and modern development practices.
  • Strong communication skills, able to convey technical and non‑technical concepts.
  • Experience with data pipelines or ETL processes.
  • Familiarity with version control (Git) and collaborative workflows.
  • Exposure to CI/CD and deployment to production.
  • Experience creating data visualisations or dashboards (Power BI, Tableau).
  • Basic understanding of web applications or frameworks (Django).
  • Previous experience working on real‑world data projects.
Personal Attributes
  • Strong problem‑solving skills, attention to detail, and willingness to debug.
  • Comfortable working as part of a team, receptive to feedback.
  • Interest in emerging AI approaches, e.g., LLMs or workflow automation.
About Social Finance

Social Finance is an ambitious not‑for‑profit organisation that designs, funds, and scales solutions to complex social problems. Working in partnership with local and national governments, funders, communities, and the social sector, we tackle challenges such as homelessness, domestic abuse, child services, health, employment, and skills.

Our Values

We believe in curiosity, empathy, and pioneering spirit to create lasting change.

Working at Social Finance

The fixed salary for this position is £40,000 per annum. In addition to pay, you will be part of a mission‑driven culture, collaborative environment, and opportunities for training and career progression.

Equity, Diversity & Inclusion

We actively encourage applications from under‑represented and minoritised groups, including those with lived experience of the social issues we address. We are an equal opportunities employer.

Flexible Working

Flexible working options are available. We welcome UK‑based applications from outside London or the South East if they can meet in‑person meeting requirements. Secondments and part‑time work are also accommodated.

Closing date for applications: Thursday 8 July 2026, 5pm.

Data Scientist in London employer: Social Finance

Social Finance is an exceptional employer that fosters a mission-driven culture, offering a collaborative environment where you can grow your skills as a Data Scientist. Located in London, you'll have the opportunity to work on impactful projects addressing complex social issues while receiving structured support and training for your professional development. With flexible working options and a commitment to equity, diversity, and inclusion, Social Finance is dedicated to creating a workplace where everyone can thrive.

Social Finance

Contact Details:

Social Finance Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Scientist in London

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Social Finance!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Scientist at Social Finance.

Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Social Finance.

Apply Directly through Our Website

When you find a suitable opening like Data Scientist at Social Finance, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Data Scientist in London

SQL
Python
Problem-Solving Skills
Communication Skills
Data Engineering
Automation
Data Pipeline Development

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Social Finance, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Social Finance. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Social Finance

Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Social Finance!

Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.