Insight Manager

Old Bailey
1 year ago
Applications closed

Related Jobs

View all jobs

16. 12 Quantitative Insight Manager Filter-MARKET RESEARCH

Head of Finance and Business Intelligence

Delegated Authority Data Governance & Bordereaux Manager

Delegated Authority Data Governance & Bordereaux Manager

Bordereaux & Delegated Authority Data Governance Lead

Data Governance Leader: Quality, Compliance & Insight

Globally developing Market Research Company seeks for an Insight Manager
【Title】
Insight Manager
【Location】
London
【Salary】
£- 45,000
The Insight Manager plays an integral role in achieving successful project outcomes for clients. They work closely with Project Directors while managing Insight Executives to ensure projects run timely and smoothly with accurate, insightful project outcomes that meet our client’s needs. They are generally the key client contact and are expected to take a key role in analysis and reporting so an ability to think strategically is important.
【Key Tasks/ Responsibilities】

  • Play a key role in project brainstorms and contribute to the development and amendment of research materials, working with the Project Director and clients to achieve optimal outcomes.
  • Attend and occasionally run immersion sessions to understand strategic issues and research objectives
  • Attend and contribute to project/client pitches and credentials presentations
  • Oversee all stages of projects, ensuring that ISO standards are met, ensuring fieldwork runs smoothly and project timings are followed closely
  • When project issues/challenges arise, suggest solutions and collaborate towards a positive outcome
  • Ensure that Insight Executives and data analysts produce data that is clean and correctly coded before analysis or client delivery
  • Work on PowerPoint report development, and work with design team for additional report outputs
  • Attend and contribute to client presentations, build and maintain proactive relationships with clients, providing regular updates
  • Work with the Project Director to develop recommendations and implications for future client strategy, identifying opportunities for account expansion
  • Mentor, supervise, contribute to training, and participate in learning and development activities of Executives and Graduates
  • Line management and conducting formal appraisals of Executives
  • Support senior team members to design optimal research, developing proposals based on client briefs, sourcing costs from external suppliers – understand and manage project pricing, costing and budget
  • Contribute to marketing initiatives and proprietary research, demonstrate clear understanding of company targets and how each role contributes to achievement of these

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.

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.

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.

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.