Applied Scientist - AI/ML

We are looking for Applied Scientists across different experience levels to design, develop, experiment with, and evaluate advanced AI/ML solutions that solve real-world business problems. The role involves taking machine learning problems from problem framing and experimentation through model development, evaluation, and production deployment. Depending on experience, the successful candidate may also contribute to scientific leadership, mentor other team members, and help establish best practices across applied research and machine learning delivery. We are looking for candidates with strong foundations in machine learning who can balance state-of-the-art techniques with practical business requirements, delivering reliable and measurable AI/ML solutions. Requirements Key Responsibilities Translate business problems into well-defined machine learning problems with clear objectives and measurable success criteria. Design, develop, train, and evaluate machine learning and AI models based on business requirements. Apply appropriate techniques across areas including: Classical Machine Learning Deep Learning Natural Language Processing (NLP) Computer Vision Generative AI Large Language Models (LLMs) Define experimentation strategies, establish appropriate baselines, and conduct rigorous model evaluation. Perform statistical analysis, experimentation, error analysis, and model validation to assess model performance. Work closely with Data Engineers to define data requirements, feature pipelines, and dataset quality standards. Collaborate with Software Engineering and MLOps teams to productionise machine learning models and AI solutions. Contribute to model monitoring, performance tracking, model lifecycle management, and continuous improvement. Apply responsible AI principles, considering fairness, explainability, robustness, privacy, and reliability. Communicate technical findings, experimental results, trade-offs, and recommendations clearly to both technical and non-technical stakeholders. Contribute to technical documentation, research, experimentation, and knowledge-sharing activities. For experienced candidates, provide scientific leadership, review ML work, mentor junior scientists, and help raise the overall technical standard of the team. Required Technical Skills Machine Learning & AI Strong understanding of machine learning and deep learning concepts, including experience with relevant techniques across: Supervised and unsupervised learning Classification and regression Model evaluation and validation Feature engineering Deep learning NLP Computer Vision Generative AI / LLM-based applications The specific depth expected will vary based on the candidate's experience level. ML Frameworks Experience with one or more of the following: PyTorch TensorFlow scikit-learn Strong candidates should demonstrate the ability to select appropriate frameworks and modelling approaches based on the problem being solved. Programming Strong proficiency in Python. Experience developing machine learning experimentation and modelling workflows. Ability to write clean, maintainable, and reproducible code. Experimentation & Model Evaluation Strong understanding of experimental design. Statistical analysis and hypothesis-driven experimentation. Model evaluation and benchmarking. Baseline development. Error analysis. Model validation and performance optimization. Production ML & MLOps Experience with taking ML models or AI solutions from experimentation into production, including: Model deployment Model monitoring Model lifecycle management MLOps workflows Collaboration with engineering and data teams Cloud-based machine learning environments Experience with cloud ML platforms and MLOps tooling is highly desirable. Generative AI / LLM Experience Experience with Generative AI and LLM-based solutions will be highly valued, particularly experience taking such solutions beyond experimentation into production. Relevant experience may include: LLM-based applications Generative AI solutions Model evaluation Prompt-based experimentation AI application development Production deployment and monitoring of GenAI solutions Responsible AI Candidates should understand the importance of responsible AI and, where relevant, demonstrate experience considering: Fairness Explainability Robustness Privacy Model reliability Responsible model deployment Collaboration & Stakeholder Management