Lead Data Scientist

🕒 il y a 2 jours

🇬🇧 Royaume-Uni – Télétravail

⏰ Temps Plein

🟠 Senior

📊 Data Scientist

👻 Score fantôme 15%

Ce que ça veut dire : la probabilité que cette offre reste ouverte sans recrutement actif.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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Sedona Digital

Site WebLinkedInTous les Emplois

51 - 200 employés

💼 Conseil

🏥 Santé

📦 Logistique

Consulting • Healthcare • Logistics

Sedona Digital est une société de conseil en technologie orientée processus qui aide les entreprises à définir leur stratégie, à développer des produits basés sur l'IA et les données, à moderniser les applications et à sécuriser et gérer les environnements cloud natifs. Elle propose des services en stratégie technologique, architecture et ingénierie des données, IA et analyses prédictives, ingénierie de produits logiciels, migration vers le cloud et services gérés Azure, DevOps/CICD, ainsi que des capacités de sécurité et de centre opérationnel de sécurité (SOC) géré pour assurer des opérations et une conformité continues.

Description

• Lead the design and delivery of enterprise-scale AI, machine learning, and advanced analytics solutions • Lead a data science or Data & AI team/function • Establish and own best practices across applied data science and data-for-AI techniques • Act as a consulting data architect for data science and data-for-AI framework design and implementation • Design and leverage data services for enterprise analytical and AI requirements, including governance metadata • Translate business problems into analytical solutions and identify opportunities for predictive modelling, optimisation, and data-driven decision-making • Design, develop, and deploy machine learning models • Engineer prompts for securely hosted AI models and leverage LLM analytical capabilities • Apply statistical methods and experimentation techniques, including hypothesis testing and A/B testing • Conduct exploratory data analysis to quantify data asset value and identify patterns, trends, and key drivers • Engineer features and prepare datasets to improve model performance and robustness • Evaluate and optimise models using metrics, cross-validation, and tuning strategies • Ensure model explainability and interpretability and communicate results to technical and non-technical stakeholders • Design and implement MLOps practices including model versioning, monitoring, and retraining • Collaborate with data engineers to access, prepare, and scale cloud-platform datasets • Present insights and recommendations through data visualisation and storytelling • Contribute to analytics and AI solution design focused on business value • Engage stakeholders and clients during discovery, experimentation, and solution design

🎯 Exigences

• 10 years’ working in data-orientated enterprise technology delivery or architecture • 5 years’ working as a senior data scientist or engineer delivering DS, ML or Advanced Analytics • 2 years’ working with GCP data technologies • Machine learning techniques including regression, classification, clustering, and time series • Statistical analysis and modeling with production deployments • End-to-end ML lifecycle experience: data preparation, modeling, evaluation, deployment, and monitoring • Model performance tuning and validation techniques • SQL skills and experience working with large datasets • AI metadata service design and engineering • Proven ability to lead data teams from design to iterative program delivery and team management • Proven ability to elicit, analyse, and document requirements and processes • Applied data techniques including identification, pipelining/ETL, curation, chunking, modelling, data quality, cataloguing, lineage, and package deployment • Hands-on experience with Agile methodologies and participation in Agile ceremonies • Ability to work independently and lead a small, multidisciplinary team • Strong problem-solving skills and attention to detail • Ability to communicate complex data opportunities, AI, and analytical concepts clearly to business stakeholders up to C-level • Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field • Preferred: experience with Generative AI, RAG, Agentic AI, banking, financial services, insurance, AI governance, metadata management, data cataloguing, multi-cloud platforms, client-facing workshops, solution design, pre-sales, and relevant certifications • Python and SQL • GCP technologies: Dataflow, Dataproc, BigQuery, Dataplex, Looker, Vertex AI, Gemini • Azure technologies: ADF, Synapse, AzureML, Databricks, Purview, Power BI, AzureGPT or Claude • CI/CD with Jira, Azure DevOps, and Git repositories

🏖️ Avantages

• Invest in learning and long-term careers • Open, collaborative culture • High-performance data platforms and cloud infrastructure exposure