Senior Data Scientist
- Location17th Floor, 5 Aldermanbury Square, London, EC2V 7HR
- Work typeNot stated
- Posted12 November 2026
- SourceArtefact's careers page (Greenhouse)
Last checked43 min ago: still listed on Artefact's careers page. First seen 43 min ago.
At a glance
- EducationBachelor's degree
- Skills and toolsMachine learning, NLP, Python, SQL, AWS, Azure, Google Cloud, Terraform
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About the role
Who we are
- Artefact is a leading global consulting firm dedicated to accelerating the adoption of data and AI. We work with a variety of businesses, from supermarket chains, to private equity firms and telecoms; including Nissan, L'Oréal, Carrefour, WHSmith, Orange, Beiersdorf, BNP Paribas, and Samsung.
- Our success stems from combining advanced data technologies, agile methods for quick delivery, and dedicated teams of data scientists, data engineers, business consultants, and data analysts.
- Our 1,800 employees operate in 25 countries (Americas, Europe, Asia, Middle East, India, Africa) and we partner with 1,000+ clients .
What you will be doing
As a Senior Data Scientist in our London office , your role will encompass:
- Designing and implementing advanced data science and machine learning solutions to solve complex business problems.
- Taking ownership of project streams, from defining technical deliverables and timelines to presenting updates to client steering committees.
- Supervising and mentoring team members on code, deployment, and best practices.
- Architecting and deploying robust, scalable solutions using modern cloud technologies and MLOps principles.
Qualifications
Necessary education and experience
- Education : A Bachelor's or Master’s degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative field.
- Project & Team Leadership : Demonstrable experience supervising team members, taking responsibility for project delivery, defining technical tasks, and presenting project updates to both internal and client stakeholders.
- Advanced Modelling : Proven ability to implement a range of complex models such as time-series forecasting, gradient boosting, clustering, NLP, and Bayesian inference.
- ML-Ops & Orchestration : Strong experience with MLOps tools for orchestration, experiment tracking, hyper-parameter tuning, and deploying automated model retraining pipelines.
- Programming & Data Engineering : Proficiency in object-oriented Python, advanced dataframes (Polars/Pyspark), and data versioning (DVC). Experience designing data storage solutions and using object-oriented SQL interfaces.
- Cloud & DevOps : Hands-on experience with at least two major cloud providers (AWS, Azure, GCP), including app deployment, database services (e.g., RDS, CosmosDB), and infrastructure-as-code (Terraform). Solid understanding of CI/CD for testing and containerisation.
Desirable experience
- Advanced Education : A Master's degree or PhD in a relevant field is a strong plus.
- Parallelisation & Performance : Experience with parallelisation frameworks like Pyspark or Ray.
- Advanced Cloud & Infrastructure : Familiarity with serverless deployments (e.g., Fargate, Lambdas), infrastructure automation with Terratest or Ansible.
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