Data Science Engineer
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Role : ML/ Data Science Engineer
Rate : £500 - £550 Inside IR35
Location : Central London, hybrid 3 days per week on-site
Duration : 6 months initial contract
About the role
We're looking for a hands-on ML / Data Science Engineer to design, build and optimise production ML models on AWS. You'll work across the full model lifecycle - from choosing the right algorithm through to tuning, deployment and monitoring - using classical ML techniques.
This role will suit someone who can explain not just what they built, but why: which models they chose, the trade-offs, and how they measured success in production
Key responsibilities
Design, build and validate classical ML models (e.g. regression, tree-based ensembles, clustering) to solve real business problems
Select appropriate algorithms and clearly justify model choices to technical and non-technical stakeholders
Develop clean, well-tested, production-ready Python code
Build, train and deploy models on AWS using Amazon SageMaker and related services
Tune model performance through hyperparameter optimisation, feature engineering and error analysis
Instrument models in production, defining and tracking MLOps metrics such as accuracy, drift, latency and throughput
Collaborate with data engineers, data scientists and product teams to take models from prototype to production
Essential skills & experience
Classical / traditional ML: Proven experience with models such as linear and logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), SVMs and clustering. You can explain which models you've used, why you chose them and what alternatives you considered.
Python: Strong proficiency in Python and its data/ML ecosystem (pandas, NumPy, scikit-learn), writing maintainable, production-quality code.
AWS: Hands-on experience building, training and deploying ML models on the AWS stack, ideally with experience in Amazon SageMaker
Model tuning & MLOps: Practical experience in hyperparameter tuning and performance optimisation. You can articulate the metrics you'd track for MLOps (e.g. model accuracy, precision/recall, data and concept drift, latency, cost) and how you've instrumented models to monitor and improve performance in production. xehkeey £500.00 - £550.00 / day
Talent International UK and it's subsidiaries, Digital Gurus, Infinite Talent and Rethink act as an employment agency for permanent recruitment and employment business for the supply of temporary workers. By applying for this opportunity, you accept the T&C's, Privacy Policy and Disclaimers which can be found at .uk
Role : ML/ Data Science Engineer
Rate : £500 - £550 Inside IR35
Location : Central London, hybrid 3 days per week on-site
Duration : 6 months initial contract
About the role
We're looking for a hands-on ML / Data Science Engineer to design, build and optimise production ML models on AWS. You'll work across the full model lifecycle - from choosing the right algorithm through to tuning, deployment and monitoring - using classical ML techniques.
This role will suit someone who can explain not just what they built, but why: which models they chose, the trade-offs, and how they measured success in production
Key responsibilities
Design, build and validate classical ML models (e.g. regression, tree-based ensembles, clustering) to solve real business problems
Select appropriate algorithms and clearly justify model choices to technical and non-technical stakeholders
Develop clean, well-tested, production-ready Python code
Build, train and deploy models on AWS using Amazon SageMaker and related services
Tune model performance through hyperparameter optimisation, feature engineering and error analysis
Instrument models in production, defining and tracking MLOps metrics such as accuracy, drift, latency and throughput
Collaborate with data engineers, data scientists and product teams to take models from prototype to production
Essential skills & experience
Classical / traditional ML: Proven experience with models such as linear and logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), SVMs and clustering. You can explain which models you've used, why you chose them and what alternatives you considered.
Python: Strong proficiency in Python and its data/ML ecosystem (pandas, NumPy, scikit-learn), writing maintainable, production-quality code.
AWS: Hands-on experience building, training and deploying ML models on the AWS stack, ideally with experience in Amazon SageMaker
Model tuning & MLOps: Practical experience in hyperparameter tuning and performance optimisation. You can articulate the metrics you'd track for MLOps (e.g. model accuracy, precision/recall, data and concept drift, latency, cost) and how you've instrumented models to monitor and improve performance in production. xehkeey £500.00 - £550.00 / day
Talent International UK and it's subsidiaries, Digital Gurus, Infinite Talent and Rethink act as an employment agency for permanent recruitment and employment business for the supply of temporary workers. By applying for this opportunity, you accept the T&C's, Privacy Policy and Disclaimers which can be found at .uk