Pricing Analyst

Hemel Hempstead
9 months ago
Applications closed

Related Jobs

View all jobs

Trainee Data Analyst - Training Course

Trainee Data Analyst - Training Course

Data Analyst – Asset Optimisation

Quantitative Analyst (Equities & Equity Derivatives - VP)

Data Analyst – Asset Optimisation

Quantitative Analyst (Equities & Equity Derivatives - VP)

We are thrilled to offer a fantastic opportunity for a Pricing Analyst to join our clients team. Our client is looking for a seasoned Pricing Analyst to lead the design, implementation, and continuous improvement of pricing strategies that drive profitability while maintaining market competitiveness. This role is pivotal in strengthening pricing intelligence and enabling real-time, data-driven decision-making to support business growth.

Role: Pricing Analyst
Salary: Upon Application
Location: Hemel Hempstead

Key Responsibilities:

Create and maintain a standardised pricing structure for all products, including differentiated tiers (Gold, Silver, Tail, Web).
Implement dynamic pricing models responsive to market conditions to optimise margin performance.
Ensure pricing includes full landed cost components such as freight and import duties.
Design, implement, and manage pricing models to support bids, tenders, and quoting processes.
Conduct in-depth analysis of historical sales, market trends, competitor activity, customer segmentation, and product lifecycle data to inform strategic pricing decisions.
Collaborate cross-functionally with sales, finance, and procurement teams to ensure pricing decisions are commercially and operationally viable.
Monitor customer-specific pricing and margin performance, flagging opportunities for improvement.
Maintain pricing data integrity within ERP and reporting systems.
Deliver regular reports and dashboards with insights into pricing KPIs, profitability, and market trends.
Required Skills & Experience for the role:
All Applicants Must hold the right to work and live in the UK.

Bachelor's degree in Business, Finance, Economics, Mathematics, or a related quantitative field.
Further certification in data science, pricing strategy, or analytics tools is desirable.
Proven experience in pricing, commercial, or financial analytics roles, ideally across FMCG, e-commerce, or related sectors.
Advanced technical expertise in Python, R, SQL for data analysis and automation.
Strong modelling capabilities including A/B testing, elasticity modelling, segmentation, clustering, sensitivity/scenario analysis, and conjoint analysis.
Proficiency in Advanced Excel, including Macros/VBA and Power BI.
Demonstrated ability to work with large, complex datasets and translate findings into commercial insights.
Excellent collaboration and stakeholder management skills.
Familiarity with ERP systems and pricing databases.If you are interested in applying for this position and you meet the requirements, please send your updated CV to: Melanie Cave at Line Up Aviation -
Line Up Aviation has carved its own place in the recruitment of Aviation and Aerospace personnel all over the world for more than 30 years. We work with some of the industry's best-known companies who demand the highest standard of applicants.
"Follow @LineUpAviation on Twitter for all of our latest vacancies, news and pictures from our busy UK Head Office. Interact with us using the #LineUpAviation tag at anytime! Thank you for your follow

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.