Lead Data Scientist

🕒 vor 2 Tagen

🇬🇧 Vereinigtes Königreich – Remote

⏰ Vollzeit

🟠 Senior

📊 Data Scientist

👻 Geisterscore 15%

Was es bedeutet: wie wahrscheinlich diese Stelle offen bleibt, ohne dass aktiv eingestellt wird.So liest du es: 60 % oder höher heißt, wir halten das für einen Geisterjob. Niedrigere Werte sind weniger bedenklich.Wie wir werten: Dauer offen, Neuveröffentlichungen, offene Stellen vs Teamgröße, wie schnell Stellen geschlossen werden, und ob ein Gehalt angegeben ist.Hinweis: eine Schätzung aus öffentlichen Daten, kein Urteil über das Unternehmen. Echte Stellen können hoch punkten, nutze es also als Hinweis, nicht als Fakt.

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Sedona Digital

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

💼 Beratung

🏥 Gesundheitswesen

📦 Logistik

Consulting • Healthcare • Logistics

Sedona Digital ist ein prozessorientiertes Technologieberatungsunternehmen, das Unternehmen dabei unterstützt, Strategien zu definieren, KI- und datengesteuerte Produkte zu entwickeln, Anwendungen zu modernisieren und cloud-native Umgebungen zu sichern und zu betreiben. Sie bieten Dienstleistungen in den Bereichen Technologietransformation, Datenarchitektur und -technik, KI und prädiktive Analytik, Softwareproduktentwicklung, Cloud-Migration und Azure-verwaltete Dienste, DevOps/CICD sowie Sicherheits- und Managed-SOC-Fähigkeiten, um fortlaufende Betrieb und Compliance zu gewährleisten.

Beschreibung

• 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

🎯 Anforderungen

• 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

🏖️ Vorteile

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