Enterprise Sales Executive

Wilson Grey
Newcastle upon Tyne
11 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Principal Data Architect

BI & Data Warehouse Developer

Enterprise Data Architect - Oracle Fusion

Enterprise Data Architect

Lead Enterprise Data Architect

Enterprise Sales Executiveopportunity with a fast-growing AI startup. This is a remote role based in the UK.


  • Have you sold a highly technical SaaS or AI product?
  • Do the personas you've sold to include Data Scientists, Heads of Engineering and other technical stakeholders?
  • Have you exceeded ARR targets of £1mil?


If so, you will be a great fit for this role.


This position requires an experienced enterprise salesperson who has sold highly technical SaaS products or complex platforms, ideally with AI. You are someone who is skilled in guiding clients through cutting-edge technology and smashing your own sales targets.


Our client is an innovative tech company at the forefront of artificial intelligence transformation, empowering enterprises to unlock new possibilities through advanced AI and computer vision solutions.


As an Enterprise Sales Executive, you'll be a critical bridge between technical customers and our client’s AI solutions. You will take on a 360 sales role and directly engage with clients to understand their needs, deliver technical insights, and maximize the value of the platform.


This role combines sales skills, hands-on technical support and proactive customer education, enabling you to create a real impact for businesses adopting AI technology.


About the role:

  • End-to-end sales initially, from identifying prospects, lead generation, through demos, managing the deal and closing (you will supported by an SDR as the team grows)
  • Communicate directly with clients to understand their needs, propose solutions based on our client’s technology, provide technical guidance, and maximize platform benefits
  • Conduct meetings remotely and in person when appropriate
  • Run technical demos tailored to address customer challenges and goals, and provide sales team training on demos
  • Develop resources, conduct webinars, and create presentations and videos to inform customers about the client’s AI platform
  • Collaborate with R&D and Account Executives, and report on customer needs, market trends, and new product opportunities


About you:

  • Several years in technical B2B enterprise sales or a commercial sales engineer role selling to enterprises
  • Bachelor's degree, preferably in a technical field, or equivalent experience
  • Recent experience in a tech startup or high-growth scale-up
  • You enjoy the entire sales process, from initial prospect research through to closing deals
  • Demonstrable track record of closing deals and hitting sales targets of £1mil+ ARR
  • Technical background or have sold a complex product to a technical audience
  • Based in the UK full-time


On offer:

  • Base salary of £85k - £110k + Double OTE
  • Health Insurance
  • Gym membership
  • Flexible, remote working

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.