ML EngineerleadEngland, United KingdomhybridcontractIT Services and IT Consulting and Artificial IntelligencePythonAutoMLAWS SageMakerMLOpsData EngineeringPredictive AnalyticsAWS ECSAWS Lambdaposted 25 Jul
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.
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