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.