Statistical Data Scientist

Statistical Data Scientist

Department: Data Science & Analytics

Employment Type: Permanent - Full Time

Location: London

Reporting To: Lead Statistical Data Scientist

Oritain is a global leader in forensic origin verification of products and raw materials. With offices in Auckland, Dunedin, London, Paris, Singapore and Washington

D.C, our vision is to be the source of truth in global supply chains.

Through our proprietary methodology, our mission is to harness cutting edge science, data, and specialized services to create a community of origin verified buyers and suppliers, protecting our people and planet. We empower the world's leading brands to make positive changes across their supply chain; ensuring product integrity, meeting regulatory demands, and reducing the risk of fraud and unethical sourcing - creating real change in our world.

About the Role

As a Statistical Data Scientist, you'll build the statistical models and machine learning methods behind our origin-verification science, then work with teams across Oritain to get those models running within live products and workflows.

You'll partner with scientists, engineers, product teams and business stakeholders to turn complex data into solutions that solve real customer problems. From developing predictive models to improving analytical approaches and supporting decision‑making, your work will have a direct impact on our products, customers and future growth.

This role sits in a business that is growing fast. We're building, evolving and scaling. That means you'll have the opportunity to shape how things are done, influence decisions, and contribute well beyond the boundaries of a traditional data science role.

If you're looking for an environment where ownership is encouraged, ideas are welcomed, and your work can genuinely move the business forward, Oritain is the right place for you.

• Collect, clean and process data from a range of sources.
• Build statistical models and machine learning algorithms that turn data into decisions.
• Develop and deploy predictive models to solve operational and commercial challenges.
• Build visualisations, dashboards and analytical tools that make insight easy to use.
• Work closely with cross-functional teams to embed analytical solutions into existing products and workflows.
• Evaluate and improve modelling approaches using rigorous quantitative assessment.
• Track developments in statistics and data science and bring the useful ones into our analytics.
• Provide expert guidance on data science initiatives across the organisation.

Essential:

• A degree in Statistics, Mathematics, Machine Learning, Data Science or a related field (postgraduate study preferred).
• A minimum of 2 years of commercial experience applying statistical modelling and machine learning techniques to real‑world problems.
• Solid grounding in probability, statistics, uncertainty, linear algebra and calculus.
• Experience working with large datasets, data transformation and cleaning.
• Experience with classification, clustering, dimension reduction and other machine learning methods.
• Experience using software development tools such as VS Code, Conda and Git Hub, and working with command line tools and Unix‑based operating systems, including environment management.
• Experience using Python and R to develop and deploy models in cloud environments (Azure experience is advantageous).
• Understanding of machine learning life cycles and MLOps principles.
• Experience working with databases, APIs and collaborative development tools.
• Sharp problem‑solving and analytical skills, with attention to detail and sound judgement.
• Confident communication skills, including the ability to explain technical concepts to non‑technical audiences.
• An open, curious and learning‑oriented approach, with a commitment to doing great work.

Nice to have:

• Experience with Databricks or similar platforms.
• Experience with HTML, JavaScript or related technologies.
• Exposure to supply chain, sustainability or risk‑focused analytics environments.

• Hybrid working (minimum 3 days per week in our Farringdon office)
• 35 days paid leave, inclusive of public holidays
• Birthday off
• Enhanced Maternity and Paternity Leave
• Life insurance
• Healthcare Cash Plan
• Employee Assistance Programme (EAP)
• Pension
• Monthly Wellbeing Allowance
• Breakfast, snacks, Friday lunch and barista coffee in the office
• Learning portal with over 100,000 assets for professional development