Data Analyst

Chambers & Partners
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Overview: The Data Analyst will play a key role in transforming Chambers into a data-driven insights business. Sitting within our Data Platform teams, this role will enhance the understanding of complex data sets while collaborating closely with BI, Product, and Data Engineering teams.

Main Duties and Responsibilities:

You will excel at sourcing, analysing, and deriving insights from data to support product development, internal reporting, and data engineering initiatives.

Key Responsibilities:

  • Analyse and interpret complex datasets using SQL, Python, Databricks, and data modelling techniques.
  • Work with data warehouses and data lakes, ensuring effective data integration, ETL processes, mapping, and reconciliation.
  • Conduct data profiling and quality assessments to identify issues and recommend solutions.
  • Collaborate with stakeholders to evaluate remediation options, influencing technology decisions.
  • Partner with Data Engineers, Data Analysts, and Product Owners to improve data processes.
  • Gather and define business requirements, managing end-to-end project delivery.

Skills and Experience:

  • Strong data analysis and reconciliation skills.
  • Experience working in Agile environments (JIRA or similar tools).
  • STEM degree (or equivalent experience).

Person Specification:

  • A passion for interpreting data to tell compelling stories.
  • Proactive, decisive, and delivers fast results.
  • Commercial thinker with a technical base.
  • High attention to detail.


#J-18808-Ljbffr

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.