Senior Data Scientist
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Requirements
• Education: A Bachelor's or Master’s degree in Computer Science, Mathematics, Statistics, Physics, Engineering, or a related quantitative field. • Education • 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. • Project & Team Leadership • Advanced Modelling: Proven ability to implement a range of complex models such as time-series forecasting, gradient boosting, clustering, NLP, and Bayesian inference. • Advanced Modelling • ML-Ops & Orchestration: Strong experience with MLOps tools for orchestration, experiment tracking, hyper-parameter tuning, and deploying automated model retraining pipelines. • ML-Ops & Orchestration • 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. • Programming & Data Engineering • 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. • Cloud & DevOps • Advanced Education: A Master's degree or PhD in a relevant field is a strong plus. • Advanced Education • Parallelisation & Performance: Experience with parallelisation frameworks like Pyspark or Ray. • Parallelisation & Performance • Advanced Cloud & Infrastructure: Familiarity with serverless deployments (e.g., Fargate, Lambdas), infrastructure automation with Terratest or Ansible. • Advanced Cloud & Infrastructure
Responsibilities
• As a Senior Data Scientist in our London office, your role will encompass: • Senior Data Scientist • London office • 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.