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.
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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.
Qualifications
- MSc or PhD in:
- Computer Science
- Machine Learning
- Artificial Intelligence
- Statistics
- Mathematics
- Data Science
- or another relevant quantitative discipline
- Equivalent practical industry experience may also be considered.
Experience Levels
We welcome candidates across 0–15+ years of experience.
Junior / Entry-Level
Suitable candidates may have:
- Strong academic foundation in ML/AI.
- Relevant MSc/PhD or equivalent project experience.
- Strong Python and ML framework knowledge.
- Research, thesis, internship, or practical ML project experience.
Mid-Level
Candidates should demonstrate:
- Independent ML model development.
- Strong experimentation and evaluation experience.
- Experience working with data and engineering teams.
- Exposure to production ML or MLOps environments.
Senior / Lead-Level
Candidates should additionally demonstrate:
- End-to-end ownership of production ML solutions.
- Strong scientific and technical leadership.
- Experience with GenAI/LLM applications where relevant.
- Mentoring and scientific review capabilities.
- Strong stakeholder communication and decision-making skills.
