At a Glance
- Tasks: Drive operational efficiency and optimise supply chain through hands-on analytics.
- Company: A high-growth, mission-led business in the circular economy.
- Benefits: Career progression, flexible work, and a chance to make a real impact.
- Other info: Mentor junior analysts and shape data usage across operations.
- Why this job: Join a data-first team focused on sustainability and customer care.
- Qualifications: Experience in operations or supply chain analytics and strong communication skills.
The predicted salary is between 60000 - 80000 £ per year.
A high-growth, mission-led business in the circular economy. They're data-heavy, operationally complex, and scale fast across global markets. Tech, logistics, and sustainability all collide here — and analytics is at the heart of how they drive margin, reduce waste, and serve customers better.
This is a hands-on Lead Analyst role driving operational efficiency and supply chain optimisation. You’ll own analytics across stock, fulfilment, supplier performance and logistics — identifying where costs can be cut and performance scaled.
- You’ll run experiments, automate reporting, and support decision-making across fulfilment and supply. There’s also scope to specialise more on either supply-side or demand-side data depending on where you want to take your career.
- You’ll also mentor a junior analyst and play a key role in shaping how data’s used across ops.
What they’re looking for:
- Strong experience in operations, supply chain or inventory analytics
- Confident designing and analysing A/B tests
- Clear communicator, able to translate analysis for stakeholders across the business
- Some experience managing or mentoring analysts
- Proactive mindset — commercially curious and not afraid to challenge the status quo
Why apply:
- Join a data-first business with real mission and scale
- Work with smart people who care about the customer and the planet
- Own high-impact analytics across global supply chain operations
- Career progression + flexibility to shape your remit
Apply now via the link or send your CV to Noah at Harnham.
Lead Analyst employer: Harnham Search & Selection
As a high-growth, mission-led business in the circular economy, this company offers an exceptional work environment where data-driven decision-making is at the forefront of operations. Employees benefit from a culture that prioritises sustainability and innovation, alongside opportunities for career progression and mentorship. With a focus on operational efficiency and a commitment to making a positive impact, this role as Lead Analyst provides a unique chance to shape analytics in a dynamic global market.
Contact Details:
Harnham Search & Selection Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Lead Analyst
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Harnham Search & Selection.
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When you find a suitable opening like Lead Analyst at Harnham Search & Selection, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Lead Analyst
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Harnham Search & Selection, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Harnham Search & Selection. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Harnham Search & Selection
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Harnham Search & Selection!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
