Data Analyst (L&D / People)

City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst (12-month FTC)

Responsible for delivering accurate, insightful reporting and dashboards for L&D and People Teams. The role involves translating complex data into actionable insights, ensuring data quality, and supporting strategic decision-making.

Key Responsibilities:

  • Develop and maintain reports, dashboards, and presentations using tools like Power BI and Excel.
  • Collaborate with stakeholders to understand reporting needs and deliver scalable solutions.
  • Ensure data accuracy and compliance with governance standards (e.g., GDPR).
  • Provide BAU and ad hoc analytics for initiatives, audits, and surveys.
  • Act as a subject matter expert on people data and proactively resolve data quality issues.

    Skills & Experience:
  • Minimum 1 year in L&D / people data analytics.
  • Advanced Excel and Power BI skills; familiarity with Cornerstone, Workday, SQL, and Power Automate desirable.
  • Strong analytical, organisational, and communication skills.
  • Ability to manage multiple priorities and present data effectively.

    In accordance with the Employment Agencies and Employment Businesses Regulations 2003, this position is advertised based upon DGH Recruitment Limited having first sought approval of its client to find candidates for this position.

    DGH Recruitment Limited acts as both an Employment Agency and Employment Business

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.