Machine Learning Engineer

Aspire Technology United Kingdom
Remote
This Job is No Longer Active This position is no longer accepting applications

Job Description

Machine Learning Engineer


Location: Fully Remote

Contract Duration: 9 Months

Day Rate: £700 per day (Outside IR35)


We are recruiting on behalf of a HealthTech company located in London, they are seeking to hire a Machine Learning Engineer for a fully remote position. This client is committed to revolutionising healthcare with innovative technology solutions, and using machine learning to enhance patient outcomes and boost operational efficiency. The ideal candidate will have experience as a Machine Learning Engineer, strong Python skills, and the ability to communicate complex concepts to non-technical stakeholders.


Key Responsibilities


  • Develop, test, and deploy machine learning models tailored to healthcare applications.
  • Collaborate with cross-functional teams, including data scientists, engineers, and healthcare professionals, to identify project goals and requirements.
  • Conduct data preprocessing, feature engineering, and model evaluation to ensure high-quality results.
  • Stay updated on the latest trends and advancements in machine learning and healthcare technology.
  • Document methodologies, processes, and findings clearly for stakeholders and team members.
  • Provide insights and recommendations based on model performance and data analysis.


Required Skills and Experience


  • Proven experience as a Machine Learning Engineer or in a similar role.
  • Strong proficiency in Python and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Experience with data preprocessing, feature engineering, and model validation.
  • Knowledge of healthcare data standards and regulations (e.g., HIPAA, GDPR) is a plus.
  • Familiarity with cloud platforms (e.g., AWS, Azure) for model deployment.
  • Excellent problem-solving skills and attention to detail.
  • Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders.

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