Data Science and Engineering Professional

2 weeks ago


London, Greater London, United Kingdom Methods Analytics Full time

We are seeking a highly skilled Data Science and Engineering Professional to join our team as an MLOps Specialist. This role will be responsible for ensuring the seamless integration of machine learning models into production environments.

The ideal candidate will have expertise in AI systems, data pipelines, and deployment strategies, with a strong focus on reproducibility, scalability, and reliability.

Key Responsibilities:

  • Develop and maintain high-quality machine learning models and data pipelines
  • Collaborate with cross-functional teams to align solutions with business objectives
  • Design and implement robust CI/CD pipelines for model deployment and maintenance
  • Monitor and optimize model performance, identifying opportunities for improvement
  • Participate in threat modeling and vulnerability management exercises to ensure security and compliance

Requirements:

  • Ph.D./Master's degree in Computer Science, Data Science, or related field
  • Minimum 5 years of experience in data science, engineering, or a related field
  • Expertise in Python, ML frameworks (TensorFlow, PyTorch), and containerisation tools (Docker, Kubernetes)
  • Proven experience with CI/CD pipelines, cloud platforms (AWS, Azure), and infrastructure-as-code practices
  • Strong understanding of security, compliance, and regulatory requirements in the defence sector

Benefits:

  • Competitive salary: £80,000 - £110,000 per annum
  • A comprehensive benefits package, including private medical insurance, life assurance, and pension scheme
  • Ongoing professional development and training opportunities
  • A supportive and collaborative work environment
  • Flexible working arrangements, including remote work options

About Us:

Methods Analytics is a leading provider of data-driven solutions, dedicated to improving society by helping people make better decisions with data. We pride ourselves on our collaborative, creative, and user-centric approach to data, ensuring that our outputs are transparent, robust, and transformative.



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