Investor Relations Data Analyst FTC - Hybrid/London

twentyAI
London
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Quantitative Risk Analyst

Data Analyst

Junior Data scientist

Data Engineering Technical Lead

Senior Data Analyst

Hedge Fund Data Analyst Apprentice

twentyAI are partnered with an International Private Equity Fund focused on Mid- Market investments who are looking for a talented and detail-oriented data specialist to join. As a Data Analyst, you will be analysing large datasets related to portfolio company financials, including balance sheets, cash flows and credit metrics. You will also be building reports for investors and stakeholders with Power BI.


Key responsibilities:

  • Analyse portfolio company data from various sources using Excel and SQL
  • Develop and maintain dashboards and reports using data visualization tools (e.g., Power BI, Tableau)
  • Interpret data and provide actionable insights to support business strategies
  • Collaborate with different departments to understand data needs and deliver solutions
  • Ensure the accuracy and integrity of data and reports.


Required Skills and Experience:

  • Proficiency in SQL for querying and managing databases
  • Advanced Excel skills (pivot tables, VLOOKUP, data analysis)
  • Experience using data visualization tools (e.g., Power BI, Tableau, or similar)
  • Strong analytical and problem-solving abilities
  • Attention to detail and ability to work with large datasets
  • Excellent communication skills and ability to present findings clearly to non-technical stakeholders
  • Background and knowledge of private credit


This role will be a 6-month FTC requiring 3-4 days in-office work in Central London.

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.