Data Scientist

COREcruitment
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist Placement

Data Scientist – Venture Capital | London

We’re a London-based venture capital firm backing the next generation of transformative startups - and we’re looking for a Data Scientist to define and drive our data strategy at the highest level.


You’ll sit at the intersection of investment strategy and technology, turning complex data into insights that shape deal sourcing, portfolio management, and market foresight. This is a strategic, high-visibility role with direct impact on the firm’s investment decisions.


What you’ll do:

  • Own the end-to-end data strategy for the firm, from data infrastructure to advanced analytics and AI-driven insights.
  • Build predictive models, scoring systems, and analytical frameworks to identify top startups and emerging market opportunities.
  • Partner with investment partners and senior stakeholders to embed data-driven decision making across the firm.
  • Lead, mentor, and grow a small team of analysts and data scientists.
  • Stay ahead of market trends in data science, AI, and venture capital to maintain a competitive edge.

What we’re looking for:

  • 8+ years’ experience in data science, quantitative research, or analytics, ideally with exposure to finance, VC, or tech ecosystems.
  • Deep expertise in Python, SQL, machine learning, NLP, and data visualisation.
  • Proven track record of delivering actionable insights to senior stakeholders.
  • Strategic thinker with leadership experience and the ability to build and scale data teams.
  • Strong commercial awareness and a passion for startups and innovation.

What we offer:

  • Influence at the executive level, shaping the firm’s investment and portfolio strategy.
  • Direct exposure to top founders, investors, and market-moving startups.
  • Competitive executive compensation, bonus, and hybrid working from London HQ.
  • Opportunity to define and grow the firm’s data culture from the ground up.


#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.