Senior Digital Marketing Analyst

Harnham
London
1 year ago
Applications closed

Related Jobs

View all jobs

Marketing Data Analyst

Data Engineer

Manager Quantitative Analysis - Centre for UK Growth

Manager Quantitative Analysis - Centre for UK Growth

Manager Quantitative Analysis - Centre for UK Growth

Manager Quantitative Analysis - Centre for UK Growth

SENIOR DIGITAL MARKETING ANALYST

SALARY UP TO £65,000

LONDON

4 DAYS IN THE OFFICE

The Company

This company is a conglomerate of retailers


THE ROLE

As a Senior Digital Marketing Analyst, you can expect to be involved in the following:

  • You will be the first hire dedicated to marketing analytics & measurement within the company - lots of ownership in this space!
  • Create a framework of periodic analysis and reporting
  • Measure the effectiveness of their marketing strategies with the support of the experimentation team
  • Develop strong relationships with the wider digital analytics function, data engineering and commercial analytics


Your Skills And Experience

  • Experience in using SQL (Python is also a bonus but not necessary)
  • Experience in visualisation and dashboarding
  • Extensive experience working in a digital marketing analytics space - ideally within an e-commerce retail business
  • Understanding of MMM, attribution and customer data platforms lifetime value


Interview Process

  • Talent screen - Virtual
  • Technical screen - Virtual
  • Meeting with the Hiring Manager and the COO - F2F

How To Apply

To express your interest, send your cv to Valentine by clicking the apply button below

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.