Forecasting Specialist

Leeds ICD
11 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Specialist

Senior Data Scientist - Forecasting

Energy Data Scientist

Data Scientist

Senior Machine Learning Engineer

Junior Data Scientist

Forecasting Specialist

Arla Head Office, Leeds, LS10 1AB

Permanent, days-based role (Monday-Friday, 37.5 hours per week)

We are currently seeking a Forecasting Specialist to join our team. This role will provide essential support to our Finnish market and colleagues, whilst working alongside our UK Demand Planning team.

What do we offer?

  • Competitive salary (salary discussed at application stage)

  • 26 days holiday & Bank Holidays

  • Hybrid & flexible working

  • Pension contribution matched up to 6%

  • 4 x annual salary life assurance

  • Free to use onsite Gym

  • Access to discounted products in our Staff Shop

  • People agenda commitment to training and development

  • Flexible Benefits- buy up to 5 days additional annual leave, reward gateway scheme- discounts with various retailers via my benefit platform.

  • Most importantly - Cheese hamper at Christmas!

    How will you make an impact?

    Reporting into the Demand Planning Manager- Finland, this role will play a pivotal part in improving planning efficiency through data analytics and advanced forecasting. Responsibilities include assessing data, maintaining baseline forecasts, and applying machine learning for accurate forecasting. This requires a deep understanding of demand patterns, product lifecycles, and market trends.

    Further responsibilities include;

  • Ensure data completeness and quality.

  • Maintain and regularly review master data and planning parameters for demand planning.

  • Review automatic cleansing. Ensure final output (cleansed data) is completed in the system.

  • Generate and analyse historical demand performance reports incorporating relevant actions into future forecasting.

  • Analyze and provide initial baseline forecast for phase-in/phase-out products, considering cannibalization impacts and lifecycle changes

  • Select the most appropriate statistical models for demand segmentation, considering factors like seasonality, responsiveness, trend, and stability. Manage demand segmentation review and apply overrides if necessary (in alignment with Demand Planner).

  • Run and adjust the statistical baseline forecast & advance modeling (eg, ML), including parameter setting and forecast rollup.

  • Review and maximize demand sensing utilization.

  • Monitor and report on forecasting KPI’s (e.g., forecast accuracy, forecast BIAS, forecast value add) at multiple levels and lags and provide insights on contributing factors and improvement opportunities.

  • Provide descriptive and diagnostic insights about the previous cycle's forecast performance.

    What will make you successful

    The ideal candidate will have;

  • Strong experience within demand planning and demand planning systems (Experience with SAP IBP is a strong advantage)

  • Excellent data and analytical skills

  • Experience within a fast-paced FMCG environment is preferrable.

  • Technical proficiency

  • Possesses strong collaboration, organisation and teamwork skills

    Would you like to join us?

    If you are enthusiastic about joining our team and meet the qualifications listed above, we would love to hear from you.

    For more information please contact Olivia Pine, Talent Acquisition Partner at Arla Foods. The closing date for this position is the 19th March 2025 and only CV’s sent directly via the link will be considered

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.