Lead Data Analyst

Harnham - Data & Analytics Recruitment
London
3 days ago
Create job alert

Job Description

Lead Data AnalystLondon, Hybrid (1-2x per week) Up to £65,000

This is an exciting opportunity to join a high-growth organisation where data science directly shapes performance marketing strategy. You will lead a growing data science function, drive long-term analytical programmes, and play a key role in delivering meaningful commercial impact for well-known consumer brands.

The Company They are a specialist performance marketing and technology business known for combining deep analytical expertise with innovative data products. With a rapidly expanding team and a strong track record of industry recognition, they partner with major consumer brands to solve complex measurement, forecasting, and modelling challenges. Their culture is collaborative, modern, and driven by delivering high-quality work that genuinely moves the needle for clients. As they scale, they are investing heavily in data science, experimentation, and advanced analytics.

The Role As Lead Data Analyst, you will oversee a team of data scientists and work closely with senior stakeholders across analytics, data engineering, and technology. You will: * Lead experimentation and measurement initiatives including incrementality testing, geo-based studies, brand lift approaches, and causal inference. * Own advanced modelling projects such as MMM, LTV modelling, propensity scoring, and predictive forecasting. * Deploy models into cloud environments and integrate into advertising platforms including Google and Meta. * Guide long-term analytical roadmaps and ensure projects drive measurable commercial impact. * Partner with high-profile clients, often with direct access to raw data, applying rigorous analytical thinking to real-world marketing challenges. * Mentor and support a growing team of data scientists as they expand through the year.

Your Skills and Experience You will bring: * Strong commercial experience with SQL. * Deep knowledge of experimentation methodologies such as incrementality, causal inference, geo testing, or brand lift studies. * Hands-on experience with MMM, LTV modelling, and propensity modelling. * Ability to deploy and maintain models in cloud environments and integrate with marketing platforms. * Experience delivering impactful, long-term analytics or data science projects. * Management or mentoring experience is desirable, but they are also open to strong individual contributors ready to step up. * Exposure to attribution modelling is a bonus.

What They Offer * Salary up to £65,000. * Hybrid working with one day per week in their London office. * The opportunity to lead a growing data science function with genuine ownership and influence. * Work with large-scale datasets from major consumer brands. * A culture that invests in innovation, long-term thinking, and high-impact analytics.

How to Apply If you are ready to take the next step in your data science career, apply today to find out more.

Related Jobs

View all jobs

Lead Data Analyst

Lead Data Analyst

Lead Data Analyst - up to £70,000 + Benefits - Hybrid

Lead Data Analyst - Hybrid - Permanent

Lead Data Analyst

Lead Data Analyst

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.