Enterprise AI & Data Platform Lead Scientist

Sedona Digital Ltd

United Kingdom

Remote

GBP 90,000 - 120,000

Full time

14 days+

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Job summary

Sedona Digital Ltd is seeking a Lead Data Scientist to lead enterprise‑scale AI, ML and analytics solutions. The role combines technical leadership, solution architecture, and hands‑on data science to transform business challenges into measurable outcomes through data and AI.

You will set best practices across the Data & AI lifecycle, architect modern data for AI platforms, design governance and metadata frameworks, and guide multidisciplinary teams delivering scalable analytical and AI

Qualifications

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.
Hands‑on experience with machine learning techniques (regression, classification, clustering, time series, etc.).
End‑to‑end ML lifecycle (data preparation, modelling, evaluation, deployment, monitoring).
Model performance tuning and validation techniques.
SQL skills and experience working with large datasets.
AI metadata service design and engineering.

Responsibilities

Leadership of a data science or data & AI team/function.
Setup and own best practices for a wide range of applied data science and data for AI techniques.
Act as a consulting data architect for data science or data for AI framework design and implementation.
Design and leverage data services to solve enterprise analytical or AI requirements including governance metadata.
Translate business problems into analytical solutions, identifying opportunities for predictive modelling, optimisation, and data‑driven decision‑making.
Design, develop, and deploy machine learning models using techniques such as classification, regression, clustering, and forecasting.
Leverage LLM analytical capabilities by engineering prompts to securely hosted AI models.
Apply statistical methods and experimentation techniques (hypothesis testing, A/B testing) to validate models and insights.
Conduct exploratory data analysis (EDA) to quantify data asset value, identify patterns, trends, and key drivers within large datasets.
Engineer features and prepare datasets to improve model performance and robustness.
Evaluate and optimise models using appropriate metrics, cross‑validation, and tuning strategies.
Ensure model explainability and interpretability, communicating results clearly to both technical and non‑technical stakeholders.
Design and implement MLOps practices including model versioning, monitoring, and retraining strategies.
Collaborate with data engineers to access, prepare, and scale datasets from cloud platforms.
Present insights and recommendations through compelling storytelling and data visualisation (MI/BI).
Contribute to the design of analytics and AI solutions, focusing on delivering business value rather than infrastructure.
Engage with stakeholders and clients during discovery, experimentation, and solution design phases.