Principal Data Scientist / Principal ML Consultant

ECS Resource Group

ML EngineerleadEngland, United KingdomhybridcontractIT Services and IT Consulting and Artificial IntelligencePythonAutoMLAWS SageMakerMLOpsData EngineeringPredictive AnalyticsAWS ECSAWS Lambdaposted 25 Jul
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Principal Data Scientist / ML Consultant (Contract) Duration: 6 Months Engagement: Full-Time Contract Working Model: Hybrid (UK-based) Location: Client site attendance as required Start Date: ASAP Overview We are seeking a hands-on Principal Data Scientist / ML Consultant to lead the delivery of an existing machine learning programme through to a production-grade AWS deployment . This role requires someone who can provide technical leadership, mentor internal teams, and actively build, deploy, and operationalise machine learning solutions. Key Responsibilities * Design and develop predictive models to support commercial decision-making. * Utilise AutoML tools (e.g. AutoGluon, H2O AutoML, PyCaret) to accelerate model development. * Assess, prepare, and engineer data for modelling. * Deploy and operationalise ML solutions within AWS. * Implement MLOps practices including monitoring, retraining, and model governance. * Provide technical leadership and knowledge transfer to internal teams. * Manage stakeholder communication, providing clear recommendations and updates. Essential Skills * Strong hands-on Python and machine learning experience. * Proven track record delivering ML solutions into production. * Experience with AutoML frameworks. * Strong understanding of regression, forecasting, and predictive analytics. * Data engineering and pipeline development capability. * AWS ML deployment experience (e.g. SageMaker, ECS, Lambda). * Practical MLOps experience. * Excellent stakeholder management and consulting skills. * Experience mentoring data science teams. * AWS-native ML tooling and cloud architecture experience. * Experience delivering data-driven commercial or optimisation solutions. Deliverables * Production-grade ML solution deployed in AWS. * Established deployment, monitoring, and retraining processes. * Knowledge transfer and upskilling of the internal team.