Econometrician / Data Scientist

London

9 months ago

Applications closed

Econometrician / Data Scientist

London (Hybrid working 3 office days per week)

Salary DOE £40,000-£45,000

Additional Benefits: Gym Membership, Pension and yearly bonus

Job Reference: J12950

We're excited to be hiring for a unique opportunity to join a fast-growing, independent marketing effectiveness agency that genuinely puts its people first.
This is a chance for someone who wants to be a bigger fish in a smaller sea to step into a role where you can truly make your mark, have real influence, and accelerate your career growth as we continue to scale. With a loyal and diverse client base, and a culture built on support and empowerment, you'll be part of a team where your ideas are heard and your impact is recognised.

We're looking for a motivated and capable Econometrician / Data Scientist with 2-3 years of hands-on experience in Marketing Mix Modelling (MMM). Experience within the FMCG sector would be a bonus, but it's not essential.

Roles and Responsibilities
• This role is well-suited for candidates who have a strong analytical mindset and prefer working behind the scenes with data
• Leading the modelling process from briefing, data exploration, and variable selection through to model building, interpretation, and being involved in the presentation of results (interim and final debriefs will be presented by the Account Director)
• Creating clear and insightful output decks for both internal stakeholders and client presentations

Experience & Skills Required
• Strong econometric modelling skills using tools such as R, Python, or other statistical software packages (e.g., EViews, SAS)
• Experience with model validation, diagnostics, and performance metrics
• Ability to handle large datasets, clean and transform raw data, and apply advanced statistical techniques such as regression, lag structures, adstock, saturation, and interaction effects
• The successful candidate will be expected to take full ownership of modelling projects, from raw data ingestion through to final model delivery and client-ready outputs, with minimal supervision.

If this sounds like you then please apply!
Alternatively, you can refer a friend or colleague by taking part in our fantastic referral schemes! If you have a friend or colleague who would be interested in this role, please refer them to us. For each relevant candidate that you introduce to us (there is no limit) and we place, you will be entitled to our general gift/voucher scheme

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.

Jan 18, 2026

Careers Jobs

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

Jan 13, 2026

Jobs

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Jan 6, 2026

Education Jobs

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.