Sales Manager – Exhibitions

Marylebone High Street
9 months ago
Applications closed

Related Jobs

View all jobs

Head of Investor Data Strategy

Business Development Manager - Business Intelligence Subscriptions

Business Intelligence Manager

Engineering Manager - Data Quality & Governance

Business Development Manager - Business Intelligence Subscriptions London - Commercial

Senior Project Manager, Quantitative (Remote)

Sales Manager – Hospitality Exhibitions

£45,000 - £53,000 + Commission + Excellent Benefits

Hybrid

Leading media events business seeks a highly talented senior Exhibition Sales Manager to join their sales team both selling across an industry leading hospitality expo as well as managing a junior sales exec. This role will focus on developing a tech focused area of an industry leading show.

This position involves a large mix of key accounts and new business with regular face to face meetings at industry events and international travel 5-6 times a year.

You will lead and support part of the sales team, providing guidance, training, and development to enhance team members’ performance and professional growth.

This role has fast-track progression, within 12 months the plan is for this role to move into leadership with strategic and organisational responsibilities. We are looking for a highly drive, ambitious sales person who is results focused and has a strong interest in tech/events.

Candidate Profile:

  • Minimum of 3 years of exhibition sales experience and a proven track record of driving sales results.

  • Demonstrated success in achieving results within exhibitions and sponsorship sales, consistently meeting and exceeding targets.

  • Highly organised, with the ability to effectively prioritise and manage time to maximize productivity and achieve goals.

  • High emotional intelligence, skilled in building and sustaining strong relationships with both internal and external stakeholders.

  • Personable and enthusiastic, with a proactive, solutions-oriented approach—a true team player committed to collective success.

  • Lipton Media is a specialist media recruitment agency based in London. We specialise in all forms of b2b media sales including conferences, exhibitions, awards, summits, publishing, digital, outdoor, TV, radio and business intelligence.

    Our clients range from small start-up companies to FTSE 100 and 250 businesses.

    We work with people at every stage of their career from undergraduates looking for their first entry point into sales to senior managers and directors looking for their next challenge

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.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.