Purpose of the Role
This role is responsible for maintaining and expanding the enterprise data model, and for developing, publishing, and maintaining business-critical reports for both internal and external stakeholders. You will collaborate closely with the Data & Analytics team, business stakeholders, and subject matter experts to solve organizational challenges through reporting, analysis, and data visualization.
Key Responsibilities
- Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors
- Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup
- Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects
- Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences in a clear manner, and refine solutions based on their feedback
- Quality, Security & Compliance: Ensure that ML solutions meet quality and performance standards. Implement monitoring and logging for models in production, and proactively improve model accuracy and efficiency. Given the sensitive nature of our data, enforce data security best practices and compliance with relevant regulations (e.g. data privacy and confidentiality) in all ML workflows
About You
The ideal candidate is a self-starter and individual contributor who thrives in a global, fast-paced environment. You will be part of a team delivering market-changing online services, contributing your technical expertise and strong work ethic.
Required Qualifications & Experience
- Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field. Strong foundation in statistics and algorithms is expected
- Experience: 5+ years of hands-on experience in machine learning or data science roles, with a track record of building and deploying ML models into production. Prior experience leading projects or teams is a plus for a lead role
- Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous
- Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena/Redshift, etc.) is expected. Strong knowledge of DevOps/MLOps practices – candidates should have built or worked with CI/CD pipelines for ML, using tools like Docker and Jenkins, and infrastructure-as-code tools like CloudFormation or Terraform to automate deployments
- Soft Skills: Excellent problem-solving and analytical thinking. Strong communication skills to explain complex ML concepts to clients or management. Ability to work under tight deadlines and multitask across projects for different clients. A client- focused mindset is essential, as the role involves understanding and addressing the needs of large clients who come to us because they trust us
Preferred Experience
- Domain Knowledge: Familiarity with use-cases like document classification, contract analytics, fraud/risk modelling, or NLP on legal texts will help the engineer design better domain-tailored solutions
- Certifications: Relevant certifications such as AWS Certified Machine Learning – Specialty or AWS Solutions Architect, and any Machine Learning/Deep Learning specialisations, will be a plus (demonstrating validated expertise)
- Tools & Frameworks: Experience with collaborative software development tools and practices (Git version control, code review), and with project management tools (JIRA or similar) in an agile environment. Familiarity with other ML Ops tools (Kubeflow, MLflow, etc.) or big data processing frameworks (Spark) can be an added advantage
Rewards and Benefits
- 25 days holiday, plus bank holidays (pro-rata for part time roles)
- Salary sacrifice schemes, retail vouchers – including our TechScheme which can be used on a range of gadgets such as Smart TV’s, laptops and computers or household appliances.
- Life Assurance
- Private Medical Insurance
- Dental Insurance
- Health Assessments
- Cycle-to-work scheme
- Discounted gym memberships
- Referral Scheme