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- Tasks: Build and enhance demand forecasting models for a global supply chain.
- Company: Join a leading global consumer business focused on data-driven decisions.
- Benefits: Earn up to £850/day, enjoy remote flexibility, and work outside IR35.
- Other info: Dynamic role with opportunities to tackle complex forecasting challenges.
- Why this job: Make a real impact by improving forecasting accuracy and decision-making.
- Qualifications: Strong experience in supply chain forecasting and deploying ML systems.
The predicted salary is between 68000 - 85000 £ per year.
A global consumer business operating at scale, with a complex international supply chain and a strong focus on using data to drive planning and inventory decisions.
They already have forecasting capability in place, but are now investing in more advanced models and production systems to improve accuracy, responsiveness, and decision-making across regions and product lines.
What you’ll be doing:
- Build and improve demand forecasting models across complex supply chain data
- Design production ML pipelines and forecasting systems end to end
- Tackle cold start and new product forecasting problems
- Apply modern approaches including hierarchical forecasting and foundation models
- Work closely with business teams to ensure forecasts drive real decisions
What they’re looking for:
- Strong forecasting experience in supply chain, retail, or similar
- Proven track record deploying ML systems into production
- Strong Python and SQL skills
- Experience with hierarchical forecasting and time series methods
- Solid understanding of MLOps and model deployment
Requirements:
- Remote flexible
- Outside IR35
- Up to £850/day
Staff Data Scientist - Supply Chain Forecasting employer: Oliver Bernard
Contact Detail:
Oliver Bernard Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Data Scientist - Supply Chain Forecasting
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We can’t stress enough how personal connections can lead to job opportunities, especially in data science.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your forecasting models and ML pipelines. We all know that actions speak louder than words, so let your work do the talking when you land those interviews.
✨Tip Number 3
Prepare for the technical grill! Brush up on your Python and SQL skills, and be ready to tackle real-world problems during interviews. We recommend practicing with sample datasets to get comfortable with the types of questions you might face.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for talented individuals who can help us drive data-driven decisions in supply chain forecasting.
We think you need these skills to ace Staff Data Scientist - Supply Chain Forecasting
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your forecasting experience and any relevant projects you've worked on. We want to see how your skills in Python, SQL, and MLOps can shine through!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're the perfect fit for the Staff Data Scientist role. Share specific examples of how you've tackled similar challenges in supply chain forecasting.
Showcase Your Technical Skills: Don’t forget to mention your experience with hierarchical forecasting and time series methods. We love seeing candidates who can demonstrate their technical prowess in real-world applications.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at Oliver Bernard
✨Know Your Forecasting Models
Make sure you brush up on your knowledge of demand forecasting models, especially those relevant to supply chain and retail. Be ready to discuss specific models you've built or improved, and how they impacted decision-making in previous roles.
✨Showcase Your Technical Skills
Prepare to demonstrate your proficiency in Python and SQL. You might be asked to solve a problem on the spot, so practice coding challenges related to data manipulation and model deployment to show you can handle real-world scenarios.
✨Understand MLOps and Deployment
Familiarise yourself with MLOps principles and be ready to discuss your experience deploying ML systems into production. Highlight any challenges you've faced and how you overcame them, as this will show your practical understanding of the field.
✨Engage with Business Teams
Since collaboration is key, think of examples where you've worked closely with business teams to ensure forecasts were actionable. Be prepared to explain how you translated complex data insights into decisions that drove results.