Demand Planner

London
10 months ago
Applications closed

Related Jobs

View all jobs

IBP Data Analyst

Data Analyst – Demand Planning & Supply Chain

Junior Data Engineer

Trainee Data Analyst - job guarantee

Data Science Trainee

Trainee Data Analyst

Pod is thrilled to be partnering with a fast-growing startup seeking a Demand Planner to join their expanding London team! (3 days in the office).

In this newly created role, you'll be responsible for developing accurate demand forecasts, optimising inventory, analysing sales data, collaborating with key stakeholders, and driving continuous improvements in demand planning.

This is an exciting opportunity to be part of a dynamic, ambitious brand where you’ll have the freedom to shape your own growth and development.

In this role, 
you will...

Develop accurate short- and long-term demand plans using historical data, market trends, and collaboration across teams
Monitor and optimise stock levels, balancing availability with minimising excess and storage costs
Analyse demand patterns and sales performance, providing regular reports on forecast accuracy and trends
Work closely with key teams on promotions and market shifts; lead weekly demand planning meeting
Enhance forecasting accuracy, planning tools, and inventory management while maintaining data integrity in demand planning systems
About you...

Proven experience in demand planning or merchandising within an FMCG organisation
Strong numerical and Excel skills 
Build successful relationships internally and externally in order to enable seamless communication
Experience working in a fast-paced environment and desire for continuous improvement
Ability to handle complex analytical problems
If interested, please feel free to apply directly

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.