Data Analyst

London Reporting House
London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst Intern

Data Analyst - Graduate

Data Scientist

Claims Data Analyst

Entry Level Data Analyst

Data Analyst (Python) 


Overview 


LRH is on a mission to create a truly data-driven repo market. Backed by the Kaizen RegTech Group, our startup works with the world’s leading banks, asset managers and hedge funds to develop a pioneering data solution designed to enhance trading activities in the €23 trillion UK and European repo markets. Our innovation provides these financial institutions with an unprecedented perspective on market dynamics. 


https://londonreportinghouse.com  


Job Summary: 


We are seeking a motivated and detail-oriented Data Analyst to join our team starting immediately! This is an excellent opportunity for a recent graduate or someone early in their career to gain hands-on experience in data analysis and contribute to our data-driven decision-making processes. You will work closely with our data and engineering teams, and subject matter experts to analyse data, generate insights, and support various business functions. The role is on-site and you will be exposed to the business side of the organisation, as well as having an opportunity to get in front of clients and partners (quite a rare opportunity in the industry). 


Key Activities and Responsibilities: 


  • Collect, clean, and preprocess data from various sources. 
  • Perform exploratory analyses on financial data from real trades. 
  • Generate reports and visualisations to communicate findings to clients and stakeholders. 
  • Assist in the development and maintenance of dashboards and data tools. 
  • Cross-company collaboration to support data-driven projects and initiatives. 
  • Ensure data quality and integrity throughout. 


Experience and Qualifications: 


  • Bachelor’s degree with a quantitative component (e.g. Data Science, Statistics, Computer Science, Mathematics, Economics, Physics etc.), or equivalent experience. 
  • Proficiency in data analysis tools and languages (e.g. Python, R, SQL). 
  • Experience producing meaningful, informative data visualisations 
  • Strong analytical and problem-solving skills. 
  • Excellent attention to detail and organisational skills. 
  • Good communication and teamwork abilities. 
  • Eagerness to learn and adapt in a fast-paced fintech startup environment. 


Nice-to-haves: 


  • Internship or project experience in data modelling and analysis. 
  • Knowledge of statistical methods and techniques. 
  • Familiarity with cloud platforms (e.g. AWS, Google Cloud, Azure). 
  • Familiarity with business intelligence tools (e.g. Quicksight, Tableau, Power BI). 
  • Understanding of machine learning concepts. 
  • Interest in GenAI and its applications. 
  • Experience with version control systems (e.g. Git). 
  • Knowledge of the financial markets would be an asset. 


What you’ll get in return: 


  • A competitive salary package. 
  • Office in the heart of The City: 5-minute walking distance from Bank, Cannon Street and St. Paul's stations. 
  • Access to additional office space in London’s iconic Gherkin 5 minutes from Liverpool Street Station. 
  • 25 days’ holiday ️, as well as UK bank holidays. 
  • Well-being allowance. 
  • Build-your-skills ️ allowance. 
  • Private healthcare ❤️ + dental. 
  • Working within a fast-growing company that has a culture of empowerment, innovation and collaboration. 
  • Awesome team of financial markets experts, data analysts and engineers. 
  • Opportunity to play a key role in an exciting startup backed by the Kaizen RegTech Group. 
  • Opportunities for continuous career growth and learning. 

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Jobs

Portfolio Projects That Get You Hired for AI Jobs (With Real GitHub Examples)

In the fast-evolving world of artificial intelligence (AI), an impressive portfolio of projects can act as your passport to landing a sought-after role. Even if you’ve aced interviews in the past, employers in AI and machine learning (ML) are increasingly asking candidates to demonstrate hands-on experience through the projects they’ve built and shared online. This is because practical ability often speaks volumes about your suitability for a role—far more than any exam or certification alone could. In this article, we’ll explore how to build an outstanding AI portfolio that catches the eye of recruiters and hiring managers, including: Why an AI portfolio is crucial for job seekers. How to choose AI projects that align with your target roles. Specific project ideas and real GitHub examples to help you stand out. Best practices for showcasing your work, from writing clear READMEs to using Jupyter notebooks effectively. Tips on structuring your GitHub so that employers can instantly see your value. Moreover, we’ll discuss how you can use your portfolio to connect with top employers in AI, with a handy link to our CV-upload page on Artificial Intelligence Jobs for when you’re ready to apply. By the end, you’ll have a clear roadmap to building a portfolio that will help secure interviews—and the AI job—of your dreams.

Jobs Careers

AI Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

In today's competitive AI job market, nailing a technical interview can be the difference between landing your dream role and getting lost in the crowd. Whether you're looking to break into machine learning, deep learning, NLP (Natural Language Processing), or data science, your problem-solving skills and system design expertise are certain to be put to the test. AI‑related job interviews typically involve a range of coding challenges, algorithmic puzzles, and system design questions. You’ll often be asked to delve into the principles of machine learning pipelines, discuss how to optimise large-scale systems, and demonstrate your coding proficiency in languages like Python, C++, or Java. Adequate preparation not only boosts your confidence but also reduces the likelihood of fumbling through unfamiliar territory. If you’re actively seeking positions at major tech companies or innovative AI start-ups, then check out www.artificialintelligencejobs.co.uk for some of the latest vacancies in the UK. Meanwhile, this blog post will guide you through 30 real coding & system-design questions you’re likely to encounter during your AI job interview. This list is designed to help you practise, anticipate typical question patterns, and stay ahead of the competition. By reading through each question and thinking about the possible approaches, you’ll sharpen your problem-solving skills, time management, and critical thinking. Each question covers fundamental concepts that employers regularly test, ensuring you’re well-equipped for success. Let’s dive right in.

Jobs

Negotiating Your AI Job Offer: Equity, Bonuses & Perks Explained

Artificial intelligence (AI) has proven itself to be one of the most transformative forces in today’s business world. From smart chatbots in customer service to predictive analytics in finance, AI technologies are reshaping how organisations operate and innovate. As the demand for AI professionals grows, so does the complexity of compensation packages. If you’re a mid‑senior AI professional, you’ve likely seen job offers that include far more than just a base salary—think equity, bonuses, and a range of perks designed to entice you into joining or staying with a company. For many, the focus remains squarely on salary. While that’s understandable—after all, your monthly take‑home pay is what covers day-to-day expenses—limiting your negotiations to salary alone can leave considerable value on the table. From stock options in ambitious startups to sign‑on bonuses that ‘buy you out’ of your current contract, modern AI job offers often include elements that can significantly boost your long-term wealth and job satisfaction. This article aims to shed light on the full scope of AI compensation—specifically focusing on how equity, bonuses, and perks can enhance (or sometimes detract from) the overall value of your package. We’ll delve into how these elements work in practice, what to watch out for, and how to navigate the negotiation process effectively. Our goal is to provide mid‑senior AI professionals with the insights and tools to land a holistic compensation deal that accurately reflects their technical expertise, leadership potential, and strategic importance in this fast-moving field. Whether you’re eyeing a leadership role in machine learning at an established tech giant, or you’re considering a pioneering position at a disruptive AI startup, the knowledge in this guide will help you weigh the merits of base salary alongside the potential riches—and risks—of equity, bonuses, and other benefits. By the end, you’ll have a clearer sense of how to align your compensation with both your immediate lifestyle needs and long-term career aspirations.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.