Senior Research Executive (Quantitative)

WeAreAspire
Leeds
2 months ago
Applications closed

Related Jobs

View all jobs

Manager Quantitative Analysis - Centre for UK Growth

Quantitative Researcher - Systematic Equities

Senior Data Scientist

Senior Data Scientist & Machine Learning Researcher

Senior Data Engineer Azure Architecture and Platforms

Senior Data Engineer

Senior Research Executive (Quantitative)

Senior Talent Consultant at WeAreAspire | Hiring across Research & Insight throughout the UK and International markets


This range is provided by WeAreAspire. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.


Base pay range

Direct message the job poster from WeAreAspire


JOB TITLE: Senior Research Executive (Quantitative)
SALARY: £32k - £38k DOE
LOCATION: Leeds (2-3 days a week in the office)


THE COMPANY

We're excited to represent an award-winning agency that has consistently been recognized for both its outstanding work and vibrant company culture. This is a unique opportunity to collaborate with some of the UK's most renowned brands while being part of a team dedicated to providing top‑tier strategic insights that help clients overcome their biggest challenges and make informed decisions.


With cutting‑edge research techniques and innovative, in‑house approaches, this agency delivers impactful solutions on fast‑paced, high‑profile projects across various sectors. Their adaptability and client‑focused mindset allow them to meet a wide variety of needs.


If you're looking to make your mark at a forward‑thinking agency, this is your chance.


KEY DUTIES

  • Lead and support client projects, managing ad‑hoc and tracker studies from start to finish.
  • Deliver sharp analysis, creative outputs, and contribute to design, brainstorming, storyboarding, and reporting processes.
  • Draft proposals, explore new opportunities, and coordinate teams, suppliers, and resources within budget.

SKILLS & EXPERIENCE

  • Strong quantitative research expertise, with experience in questionnaire design, data analysis, and statistical techniques.
  • Interest in tech, or media; skilled storyteller simplifying insights with impactful data visualisation.
  • Excellent communication, critical thinking, and relationship‑building skills, with meticulous attention to detail and quality.

Interested in this Senior Research Executive/Research Manager role? Apply now and let's have a chat!


We Are Aspire Ltd are a Disability Confident Commited employer.


Seniority level

Mid‑Senior level


Employment type

Full‑time


Job function

Marketing


Industries

Market Research


Referrals increase your chances of interviewing at WeAreAspire by 2x


Get notified about new Senior Research Executive jobs in Leeds, England, United Kingdom.


#J-18808-Ljbffr

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.