Head of Investor Data Strategy

LGBT Great
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Head of IT and Business Intelligence

Head of Data Science

Head of Data Engineering (AI)

Head of Data Engineering (AI)

Head of Data Analytics and Insights

Head of Data Engineering (AI)

With Intelligence Ltd are actively recruiting for an innovative and strategic leader to fill the role of Head of Investor Data Strategy. This pivotal position will be responsible for developing and executing a comprehensive data strategy that enhances our offerings within the asset management sector.

The Head of Investor Data Strategy is responsible for owning, developing, and driving the strategic vision of the Investor dataset. This role combines data ownership, market insight, and data strategy to ensure the dataset evolves in line with client needs, market trends, and the company’s broader product objectives.

You will act as the primary point of accountability for the Investor dataset, shaping its roadmap, overseeing data quality and structure, coordinating cross‑functional stakeholders, and positioning the dataset as a market‑leading product. This role is ideal for a data‑driven leader with a strong understanding of investor ecosystems and a passion for turning data into commercial and strategic advantage.

Our organisation is a premier source of investment intelligence and data, bridging fund‑raising and business development for the alternative investments sector including hedge funds, private equity, private credit, infrastructure and real estate. We are comprised of a talented global team including analysts, reporters and industry experts, all dedicated to facilitating better capital allocation.

Key Responsibilities:
  • Data Ownership
    • Lead the strategic development of the Investor dataset, ensuring alignment with overall product strategy, customer needs, and market trends.
    • Develop and execute the dataset roadmap, defining key initiatives and priorities in partnership with Product Strategy.
    • Create and maintain client‑facing documentation and training materials to support dataset understanding and adoption.
    • Serve as the primary business contact and subject matter expert for all Investor dataset‑related matters.
  • Continuous Data Set Development
    • Lead research and development activities to continuously evolve the dataset’s content and capabilities.
    • Analyse market trends, competitor data offerings, and customer usage insights to identify improvement opportunities.
    • Partner with Data Operations, Editorial, and Technology teams to enhance data quality, tooling, and workflows.
  • Aligning Data Functions to Business Needs
    • Consolidate and prioritize business requirements into a unified dataset roadmap that maximizes impact and alignment.
    • Facilitate regular review sessions and working groups across Data Ops, Editorial, Data Management and Product to coordinate dataset initiatives.
    • Collaborate closely with Product Strategy, Sales, Insights, Marketing, and Events to align data developments with business goals.
  • Market Expertise and Representation
    • Act as an internal and external ambassador for the Investor dataset, promoting its quality, innovation, and market value.
    • Collaborate with the Insights team on reports, thought leadership, and initiatives that enhance dataset visibility and brand equity.
    • Support strategic activities such as acquisition assessments and integration planning for data‑related opportunities.
Qualifications:
  • Proven experience in data and analysis within financial services, particularly in alternative investments or the asset management industry
  • In‑depth understand and experience of Limited Partner/Investor markets would be preferable
  • Strong understanding of data governance, analytics, and management processes.
  • Excellent leadership and project management skills with a track record of driving successful data initiatives.
  • Exceptional ability to analyse data, derive insights, and communicate findings to stakeholders.
  • Proactive, curious and innovative mindset, always seeking to enhance data relevance and impact. Strong decision‑making capabilities to progress data strategy development and execution
  • Strong relationship‑building skills and the ability to work cross‑functionally.
  • Strategic thinker with a results‑oriented mindset.
  • Experience with data management tools and analytics platforms.
  • Experience in project delivery with a track record of delivering initiatives.
Benefits:
  • 24 days annual leave rising to 29 days
  • Enhanced parental leave
  • Medicash (Health Cash Plans)
  • Wellness Days
  • Flexible Fridays (Opportunity to finish early)
  • Birthday day off
  • Employee assistance program
  • Travel loan scheme
  • Charity days
  • Breakfast provided
  • Fully stocked drinks fridge
  • Social Events throughout the year
  • Hybrid Working
Our Company:

With Intelligence is based at One London Wall, London EC2Y 5EA. We offer a range of benefits and actively encourage social networks that you are free to join.

As part of our company, you will enjoy the benefits of an open plan office and working with a social and energetic team. With Intelligence provides exclusive editorial, research, data and events for senior executives within the asset management industry. These include hedge funds, private credit, private equity, real estate and traditional asset management, and our editorial brands are seen as market leaders in providing asset manager sales and IR execs with the actionable information they require to help them raise and retain assets. To maintain and grow our position in the market we need to continue to hire highly motivated, thoughtful and to ensure our subscribers are getting the exclusive intelligence they need first, and most comprehensively, through our range of services. If you are interested so far in what you have read, please apply, we look forward to hearing from you.

We are an Equal Opportunity Employer. Our policy is not to discriminate against any applicant or employee based on actual or perceived race, age, sex or gender (including pregnancy), marital status, national origin, ancestry, citizenship status, mental or physical disability, religion, creed, colour, sexual orientation, gender identity or expression (including transgender status), veteran status, genetic information, or any other characteristic protected by applicable law.


#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.

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.