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Sedona Digital

Technology transformation partner for financial services

Lead Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in UKNo salary listedRemote

About the Job

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.
  • Experience with statistical analysis and modeling in 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 AI metadata services.
  • Proven ability to lead data teams from design through iterative program delivery and team management.
  • Ability to elicit, analyse, and document requirements and processes.
  • Applied data techniques experience, including identification, pipelining and ETL, curation, chunking, modelling, 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.
  • Strong problem-solving skills and attention to detail, with the ability to make pragmatic decisions independently.
  • Ability to communicate complex data opportunities, AI, and analytical concepts clearly to business stakeholders up to C-level.
  • Comfort working in a fast-paced, changing environment.
  • A Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.

Responsibilities

  • Lead the data science or data and AI team/function.
  • Establish and own best practices for 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 for enterprise analytical or AI requirements, including governance metadata such as managed RAG, cataloguing, lineage, trust, weighting, usage, history, obsolescence, and observability summarisation for risk, compliance, FinOps, and user experience.
  • 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.
  • Engineer prompts to leverage analytical capabilities of securely hosted AI models.
  • 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 in 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 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 storytelling and data visualisation, including MI/BI.
  • Contribute to analytics and AI solution design, focusing on business value rather than infrastructure.
  • Engage with stakeholders and clients during discovery, experimentation, and solution design.

Desired Qualifications

  • Experience with Generative AI, RAG, 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.

About the Company

Sedona Digital

Technology transformation partner for financial services

Sedona Digital provides technology transformation services for financial services (banking and insurance) and enterprise clients in life sciences and IT. It organizes work into Strategy, Innovate, and Operate, helping clients create roadmaps, build software, modernize data architectures, and implement AI and cloud migrations (with Microsoft Azure). It differentiates itself by embedding senior practitioners in client teams and maintaining ISO 27001 and ISO 9001 certifications, offering a single flexible partner from strategy to ongoing operations. Its goal is to enable UK and US financial enterprises to transform and run technology platforms that are secure, compliant, and efficient.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
Not listed

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