Artefact

Senior Data Scientist

17th Floor, 5 Aldermanbury Square, London, EC2V 7HR · Work style not stated

Posted over 6 months ago: check it’s still open

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About the role

Description from the employer’s posting. Check the employer’s page for the current version.

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.

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.

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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.

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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