Marketing Mix Modelling (MMM) Consultant

Lorien
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist - Measurement Specialist

Data Scientist - Measurement Specialist

Lead Data Scientist - Marketing Science

Lead Data Scientist - Marketing Science

Lead Data Scientist - Marketing Science

Customer Campaign Data Analyst - B2C

Marketing Mix Modelling (MMM) ConsultantInside IR35 –Negotiable Rate6 Months view to extendRemote working optionsThis isa Marketing Mix Modelling (MMM) Consultant role working in aConsumer Healthcare Company as they look to improving media spendcoverage of Marketing Mix Modelling.The key responsibilities forthis role includes:Work with local stakeholders to scope MMMdeliveries (including what data to include and aligning on thescope of the models).Once the Data Scientists have complete models,deliver these models to the local stakeholders in the PowerBIreport, and work with local stakeholders to provide actionableinsights that have aide realising the value from the tool (e.g. byoptimising the media mix).Over 2025, deliver 25 BMCs models (phasedquarterly).Track value realisation with the aim to show a 5%increase in ROI from media spend.The key experience and knowledgerequired for this role includes:Number of years’ experience ofdelivering MMM to client either agency or client side.Experiencedelivering models for consumer health brands.Experience deliveringmodels for clients with a global footprint (e.g. involved inprojects across EMEA, APAC, US, LATAM etc.This is a contract roleworking for a top company as a Marketing Mix Modelling (MMM)Consultant. Please apply to the advert for moreinformation.

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.