Job Description

Job Purpose

We are looking for a passionate candidate in the application of advanced analytics and data science to address varied business problems. You will need to have a strong grounding in python and have experience of leading value delivery through product development and AI/ML modelling. You will operate in teams on a wide range of projects using agile ways of working collaboratively with other colleagues and partners from the wider Biostatistics, Data Science and R&D community.

Desired Skills and experience

Bachelor's or master's degree in computer science, Engineering, Mathematics, or a related field.

5+ years of hands-on experience in machine learning, deep learning, or AI engineering roles.

Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

Experience with cloud platforms (AWS, GCP, Azure [Preferred]) and deploying ML models in production.

Strong software engineering skills, including version control (Git), containerization (Docker), and CI/CD pipelines.

Solid understanding of data structures, algorithms, and system design.

Experience with data preprocessing, feature engineering, and model evaluation.

Excellent problem-solving, analytical, and communication skills.

Ability to work independently and as part of a collaborative team.

Key Responsibilities

Design, develop, and deploy scalable machine learning models and AI-driven solutions for real-world business problems.

Collaborate with data scientists, software engineers, and product managers to define requirements and deliver impactful solutions.

Lead the end-to-end machine learning lifecycle: data preprocessing, feature engineering, model selection, training, evaluation, and deployment.

Implement and maintain ML Ops pipelines for continuous integration, delivery, monitoring, and retraining of models.

Optimize models for performance, scalability, and efficiency in production environments.

Stay current with the latest AI/ML research, tools, and best practices; evaluate and implement new technologies as appropriate.

Document processes, models, and code to ensure reproducibility and knowledge sharing.

Mentor and guide junior engineers and team members.

Ensure compliance with data privacy, security, and ethical standards in all AI/ML initiatives.