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Tech Stack
Tools & technologiesCloudPython
About the role
Key responsibilities & impact- Developing and deploying machine‑learning models in production environments
- Working end‑to‑end across the data science lifecycle: problem definition, modelling, deployment and monitoring
- Analysing new and existing data sources to improve decision‑making and model performance
- Designing feedback loops that continuously improve outcomes and data quality
- Collaborating closely with engineers to deliver scalable, reliable model predictions
- Communicating technical insights clearly to non‑technical stakeholders
Requirements
What you’ll need- Solid experience working as a data scientist on real‑world, production problems
- Strong Python skills (or a similar language used in applied data science)
- Experience with supervised learning and unsupervised techniques such as anomaly detection
- Exposure to cloud‑based model training, deployment and automation
- A strong sense of ownership and accountability for data science outputs
- Experience working with large consumer datasets or high‑traffic platforms is an advantage, but not essential.
Benefits
Comp & perks- Flexible hybrid working
- 25 days leave, 2 days paid for volunteering and life event leave
- Competitive salary and bonus (bonus dependent on role)
- Company pension
- Enhanced parental leave
- Healthcare options
- Wide range of flexible benefits
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningPythonsupervised learningunsupervised learninganomaly detectiondata analysismodel deploymentmodel monitoringdata science lifecycledata quality
Soft Skills
collaborationcommunicationownershipaccountability