Full-Time
Lead Data Scientist
Sedona Digital
No salary listed
Remote in UK
Remote
Bachelor's, Master's
Category
 Data & Analytics (1) |
|---|
Required Skills
LLM
Claude
Power BI
MLOps
Microsoft Azure
Python
Regression
Data Science
Git
Forecasting
Figma
BigQuery
SQL
Machine Learning
A/B Testing
RAG
JIRA
Observability
Data Governance
Databricks
Looker
Data Analysis
Requirements
- At least 10 years of experience in data-oriented enterprise technology delivery or architecture.
- At least 5 years of experience as a senior data scientist or engineer delivering data science, machine learning, or advanced analytics.
- At least 2 years of experience with Google Cloud Platform data technologies.
- Hands-on experience with machine learning techniques including regression, classification, clustering, and time series.
- Statistical analysis and modeling experience with production deployments.
- Experience across the end-to-end machine learning lifecycle, including data preparation, modeling, evaluation, deployment, and monitoring.
- Experience with model performance tuning and validation techniques.
- SQL skills and experience working with large datasets.
- Experience designing and engineering artificial intelligence metadata services.
- Proven ability to lead data teams from design through iterative program delivery and team management.
- Proven ability to elicit, analyze, and document requirements and processes.
- Proven ability with applied data techniques including identification, pipelining or extract-transform-load, curation, chunking, modeling, data quality, cataloguing, lineage, and package deployment.
- Hands-on experience with Agile methodologies and active participation in Agile ceremonies such as sprint planning, retrospectives, and backlog grooming.
- Ability to work independently and own activities while leading a small, multidisciplinary team.
- Ability to communicate complex data opportunities, artificial intelligence, 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.
Responsibilities
- Lead the design and delivery of enterprise-scale artificial intelligence, machine learning, and advanced analytics solutions.
- Define best practices across the Data and AI lifecycle.
- Architect modern data-for-AI platforms.
- Design governance and metadata frameworks.
- Lead multidisciplinary teams delivering scalable analytical and AI solutions.
- 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 and AI requirements, including governance metadata such as managed retrieval-augmented generation, cataloguing, lineage, trust, weighting, usage, history, obsolescence, and observability summarisation for risk, compliance, FinOps, and user experience.
- 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 using classification, regression, clustering, and forecasting.
- Engineer prompts for securely hosted AI models using large language model analytical capabilities.
- Apply statistical methods and experimentation techniques, including hypothesis testing and A/B testing, to validate models and insights.
- Conduct exploratory data analysis to quantify data asset value and 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, and communicate results clearly to 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 data storytelling and data visualisation.
- Contribute to analytics and AI solution design with a focus on business value rather than infrastructure.
- Engage with stakeholders and clients during discovery, experimentation, and solution design phases.
Desired Qualifications
- Experience with Generative AI, retrieval-augmented generation, and Agentic AI solutions.
- Experience working within banking, financial services, or insurance.
- Knowledge of AI governance, metadata management, and data cataloguing practices.
- Experience within insurance or other regulated industries.
- Exposure to multi-cloud data and AI platforms.
- Experience supporting client-facing workshops, solution design, and pre-sales activities.
- Relevant Data, AI, or Cloud certifications.