French Data Analyst

ABL
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst - Farming Operations

Data Analyst - Farming Operations

Data Scientist

Title: French Data Analyst - Advanced Analytics & Insights

Contract: 6‑month contract

Location: Hybrid - London

Daily rate: £308.00

A leading global technology organisation is seeking a highly skilled Data Analyst to join its high‑performing analytics function. This division works at the cutting edge of digital advertising, ecommerce, and advanced data science, partnering with some of the world's most recognisable brands to deliver strategic insights that directly influence multimillion‑pound marketing decisions.

As a Data Analyst, you will act as an analytics consultant, defining learning agendas, producing advanced analyses, and presenting insights that drive performance across digital advertising channels. You'll work end‑to‑end: from coding and modelling to client‑facing storytelling.

Key Responsibilities

Analyse advertiser performance using SQL, Python/R, and proprietary datasets to uncover trends, opportunities, and optimisation strategies.
Develop and deliver insights that influence senior marketing leaders and shape full‑funnel advertising strategies.
Create and present business reviews with clear, compelling narratives tailored to both technical and non‑technical audiences.
Collaborate cross‑functionally with commercial, product, and analytics teams across Europe.
Design and execute learning agendas, including A/B tests, measurement studies, and bespoke analyses.
Champion data‑driven decision‑making and guide stakeholders on how to act on analytical recommendations.

Required Experience & Skills

This role is designed for advanced technical analysts with strong commercial and consultative skills.

Must‑Have Skills

Fluent French - required to support stakeholders in the French market.
Advanced in SQL - able to write complex queries from scratch across multiple tables.
Bachelor's Degree in Business Administration, Economics, Computer Science, Finance or related Field.
Statistical expertise - regression, modelling, experimental design, and analytical frameworks.
Advanced Python or R - capable of running statistical modelling and advanced analytics.
Strong client communication - able to present insights to senior stakeholders with clarity and confidence.
Digital advertising knowledge with an understanding of ad measurement, KPIs, and optimisation levers.Preferred Qualifications

Experience in consulting, digital advertising, or media analytics.
Familiarity with cloud technologies and large‑scale data environments.
Additional European languages

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.

Careers

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.

Jobs

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.

Education

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.

🍪 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.