Data Analyst

MBN Solutions
Nottingham
11 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst – Econometric Modelling Consultancy

Location:Primarily Remote (Based in the UK)

Salary:From £26,000-£35,000 (Depending on Experience)


MBNs client is a growing, independent econometric modeling consultancy working with high-profile clients across the UK and globally. Due to continued success, they are looking for a detail-oriented and motivated Analyst to join our dynamic team.


Role Overview:As an Analyst, you will play a key role in bridging the gap between data gathering, modelling, and generating actionable insights for clients. You’ll be responsible for working closely with clients and agencies to obtain essential data, ensuring its accuracy, and helping the team develop insights through modeling.


Key Responsibilities:

  • Liaising with clients and agencies to gather the data essential to our work.
  • Taking ownership of the data, ensuring its accuracy, and assisting the team in using it effectively.
  • Supporting the team as they model the data and generate insights and recommendations.
  • Collaborating with colleagues to refine processes and improve data handling.


We’re looking for someone who:

  • Loves Excel – you should be confident in using Excel and eager to develop your skills further.
  • Has a logical, systematic approach and enjoys developing and following processes.
  • Is detail-focused with a passion for getting the numbers right.
  • Is self-driven and comfortable working remotely, but thrives in a collaborative team-based environment.


This role would suit:

  • Someone early in their career, eager to gain hands-on experience working with marketing data.
  • A person who enjoys direct client interaction and wants to develop their client-facing skills.
  • Someone open to exploring new career paths within data and marketing analytics.


For example, you could be:

  • A media planner/buyer who enjoys working with numbers and wants to transition into marketing analytics.
  • A BI/reporting professional looking to move into analysis.
  • A recent graduate with a strong quantitative focus (e.g., economics, statistics, mathematics).


Opportunities:There are excellent opportunities for growth within the role, including progression into econometric modelling or quantitative marketing consulting.

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.