Research Director - Quantitative

Aspire
Greater London
6 months ago
Applications closed

Related Jobs

View all jobs

Manager Quantitative Analysis - Centre for UK Growth

Research Data Scientist Intern - Tesco

Research Data Scientist Intern

Research Data Scientist Intern

Research Data Scientist Intern

Research Data Scientist Intern - Tesco

The Role

In this role you will be managing a team of researchers, overseeing their work on a day-to-day basis and providing support and guidance to help them deliver high quality deliverables to clients. You will be at the forefront of managing, maintaining and building new relationships with the clients, to ensure repeat business and a smooth service throughout. Work ranges across a number of sectors including FMCG, technology, media and financial services - to name a few.

Key responsibilities:-

Responding to briefs, writing proposals and attending pitches to win business Survey design and execution Overseeing the day-to-day running of projects with your team Story telling and report writing Presenting actionable insights to your clients to support their business needs

The Candidate

Strong background working on quantitative research projects from inception through to completion Exposure and experience of qualitative or mixed method projects would be advantageous Ability to build rapport with clients and long term relationships will be essential to succeeding in this role Excellent communication skills and previous experience in people management

To find out more click apply.

We Are Aspire Ltd are a Disability Confident Commited employer

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.