Senior Data Scientist

ArtefactEcuadorOn-siteFull-timeSenior, 5–8 yearsListed 2 weeks ago

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About this 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.