Jobs in London - Senior / Lead Data Scientist • Central London

Company: PaymentGenes

Central London

PaymentGenes is proud to be partnering with a high-growth, international technology organisation to appoint a Senior / Lead Data Scientist (AI-Native, AWS ML Stack, Production-Focused).This is a strategic hire within a business scaling real-world AI solutions — moving beyond experimentation into production-grade, AI-powered systems embedded directly into enterprise workflows.If you are passionate about deploying scalable ML systems that deliver measurable commercial impact, this opportunity is for you.This role goes beyond model experimentation! You will design, deploy, and scale AI-driven solutions using modern foundation models and AWS-native machine learning infrastructure. From LLM-powered agents to predictive models embedded in automated workflows, your work will directly influence business operations at scale.You’ll operate at the intersection of modelling, engineering, and intelligent automation. What You’ll Do Model Development & AI Systems DesignDesign and train predictive models using AWS SageMakerDevelop LLM-powered systems via AWS Bedrock (including Claude integration)Build RAG pipelines combining structured and unstructured dataDevelop evaluation frameworks for accuracy, bias, and robustnessApply best practices in feature engineering and experimentation AI Agent & Workflow IntegrationArchitect reasoning agents using advanced foundation modelsUse code-generation tooling for automation logic and integration scriptingOrchestrate multi-step AI workflowsDeploy AI-powered decision layers into enterprise processesDesign human-in-the-loop feedback systems to improve performance☁️ AWS ML InfrastructureDeploy and manage models using SageMaker (training, endpoints, pipelines)Leverage Bedrock for foundation model accessImplement serverless inference with Lambda & API GatewayUtilise S3, Glue, Athena for data processingImplement CI/CD for ML workflowsMonitor performance via CloudWatch and drift detection toolingOptimise inference cost and latency Productionisation & MLOpsBuild reproducible ML pipelinesImplement model versioning and dataset trackingDesign structured output validation and guardrailsMonitor performance and trigger retraining cyclesEnsure governance, compliance, and security alignment Business Impact & LeadershipIdentify high-impact AI use casesTranslate business problems into ML system designsLead experimentation frameworks (A/B testing, uplift modelling)Mentor data scientists and collaborate closely with data engineeringCommunicate AI strategy and risk to senior stakeholders Technical EnvironmentCore Data ScienceAdvanced Python & SQLStatistical modelling & ML algorithmsFeature engineeringExperiment design & evaluationAWS ML StackSageMaker (training, endpoints, pipelines)Bedrock (foundation models incl. Claude)Lambda (serverless inference)S3, Glue, AthenaCloudWatchIAM & security best practicesAI-Native ToolingFoundation models for reasoning workflowsCode-generation tooling for automation scriptingAgent orchestration frameworksEnterprise workflow automation toolsRAG architecturesEmbeddings & vector stores What We’re Looking For6–10+ years in data science or applied MLProven experience deploying ML models into productionHands-on experience with AWS-native ML servicesExperience building LLM-powered workflows or AI agentsDemonstrated delivery of measurable business impactYou’ll Thrive If You Have:Strong problem-framing abilitySystems-level thinking beyond model accuracyAI governance awarenessClear communication across technical and executive audiencesA bias toward practical deployment over research-only outputs Example Projects You Might DeliverProduction fraud detection model deployed via SageMaker endpointInternal AI copilot powered by Bedrock and embedded into workflowsRAG-based compliance monitoring assistantAutomated revenue forecasting pipeline with retraining triggersAI-driven document intelligence system (classification + extraction) What Success Looks LikeReduced time from model prototype to productionStable, monitored ML endpoints delivering measurable ROIImproved decision accuracy in automated workflowsStrong adoption of AI-enabled tools across the businessControlled infrastructure cost per inferenceThis is a rare opportunity to build AI systems that operate at real scale within a forward-thinking technology environment.If this excites you, please reach out to the PaymentGenes team directly via LinkedIn, who can provide more detail and insight into this company's journey into AI and the opportunity.