Lead Data Scientist

🕒 August 19

🇬🇧 United Kingdom – Remote

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Sedona Digital

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Sedona Digital is a process-led technology consultancy that helps enterprises define strategy, build AI- and data-driven products, modernise applications and secure and operate cloud-native environments. They offer services across technology strategy, data architecture and engineering, AI and predictive analytics, software product engineering, cloud migration and Azure managed services, DevOps/CICD, plus security and managed SOC capabilities to deliver ongoing operations and compliance.

📋 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

🎯 Requirements

• 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

🏖️ Benefits

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