Mta Specialist

Freshminds

1 year ago

Applications closed

Our client is a leading global company specializing in premium lifestyle products across various categories, including apparel, accessories, and home. They are currently seeking an experienced MTA Analyst to join their team. Responsibilities:Leverage econometric and MTA methodologies to assess marketing effectiveness and provide strategic recommendations.Oversee data collection, including extraction, manipulation, analysis, and validation, ensuring proper use of KPIs in modeling.Build MTA models to analyze operations and drive actionable insights.Utilize Excel, SQL, and Python to process, transform, and create models, while presenting findings to stakeholders at all levels. Requirements:4-5 years of experience in an MTA environment, with expertise in Snowflake, SQL, Python, and R.Strong understanding of customer journeys from a quantitative perspective and experience in Data Science.Proven ability to translate complex analytics into actionable insights, with experience in retail or luxury retail preferred.Advanced degree in a quantitative field, with the ability to manage multiple stakeholders in a fast-paced environment. Details:Duration: 12 monthsSalary: £70,000Start Date: February 2025Hybrid working

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 9, 2026

Careers

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

Feb 2, 2026

Jobs

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

Jan 23, 2026

Education

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.