Junior Data Analyst

Reports to: Head of Data Science & Engineering

Department: Data Science & Engineering

Location: Central London office (Fitzrovia) / hybrid

Key internal stakeholders: Data Science & Engineering team, cross-LiveMore employees

LiveMore exists to challenge the unfair NOs in the mortgage world. That’s why our mission is to empower mortgage brokers to lend more by turning NOs into YESs and by maximising affordable borrowing. By doing that whenever we can, we help our customers live more and be ready for all that’s yet to come. Using our unique LiveMore Mortgage Matcher® and LiveMore Maximum Borrowing Calculator decision engine technology, we excel at overcoming challenges involving affordability, risk and Consumer Duty. We innovate for customers and mortgage brokers in need, where others don’t – like with LiveMore’s current mortgages ‘up to’, ‘into’ and ‘in’ retirement.

LiveMore is an innovative company with ambitious plans, and we know that our people are the key to achieving our goals. As evidence of our growth, in June 2025 we were recognised as one of the Top 100 Fastest Growing Companies in the UK by the Business Leaders 500. In 2025, we grew our annual lending volumes by over 80% to lend c.£350m and completed our second £200m+ securitisation.

Role: Provide analytical and data support within the Data Science & Engineering function, contributing to data manipulation, analysis, reporting, monitoring, and pipeline projects. This is a hands‑on, learning‑by‑doing role designed for a numerate graduate who is eager to build a career in data science and engineering. You will work closely with our Data Scientist and Data Engineer, supporting day‑to‑day projects. Over time, and under structured mentorship, you will develop the technical depth to take on increasingly complex assignments.

Responsibilities:

• Support recurring BAU tasks including scheduled data pulls, routine management reporting, and data quality checks, ensuring outputs are delivered accurately and on time.
• Monitor data pipelines, flag anomalies or failures and work with the team to resolve them.
• Own a portion of the data and analytical support ticket backlog.
• Write Python scripts (e.g. in Jupyter Notebooks) to support analytical and engineering tasks.
• Create SQL queries to extract and transform data and learn SQL optimisation techniques.
• Apply standard statistical techniques such as descriptive analytics, distributions, correlation, and trend analysis to datasets and produce insight summaries.
• Support modelling and segmentation work by preparing datasets, running exploratory analysis, and assisting with model evaluation.
• Document processes, queries, and outputs to build business knowledge.
• Identify opportunities to improve or automate recurring tasks and propose solutions.

Requirements:

• Degree in a quantitative discipline (mathematics, statistics, economics, computer science, or a related field) with 2:1 or above, or equivalent.
• Familiarity with Python and SQL for writing queries to extract, filter, join, and aggregate data, whether through coursework, personal projects, or internships.
• Coursework or project exposure to statistical techniques, e.g. regression, in data analysis.
• A methodical, detail‑oriented approach and willingness to sense‑check own work.
• Clear written and verbal communication and ability to summarise data findings for a non‑technical audience.

Desirable:

• Familiarity with data visualisation tools (e.g. Looker, Power BI, or similar).
• Awareness of version control principles (Git).
• Exposure to regulated financial services, e.g. internship or placement.
• Understanding of basic mortgage or lending concepts.

How you’ll grow with us:

• From day one you will work alongside experienced data scientists and data engineers in a small, collaborative team and contribute meaningfully while building the habits and skills that underpin a long‑term career in data.
• Learn to work to professional standards for code quality, documentation, and methodology by following established conventions and receiving regular, constructive feedback from senior colleagues.
• Develop commercial awareness by understanding which tasks matter most, how to manage your own workload, and how to communicate progress and blockers clearly.
• Build the habit of rigour by sense‑checking your outputs, questioning results that don't look right, and taking ownership of the quality of everything you produce.
• Grow your ability to communicate with any audience by translating technical findings into plain language for business stakeholders and learning to tailor your communication to context.
• Cultivate genuine curiosity by asking questions, learning from the answers, and carrying that learning independently into your next piece of work.

Benefits and environment:

• Growth shares.
• Annual performance bonus (cash and growth shares).
• High‑quality working environment in a central London office in the heart of Fitzrovia.
• Hybrid working (2 days per week in office).
• 30 days’ holiday, plus UK bank holidays.
• Private health cash reimbursement plan.
• Employer pension contribution.
• Salary‑share pension scheme, saving marginal rate on contributions.
• Salary‑share nursery voucher scheme, as above.
• Employee assistance programme.