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.

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 SkillsMachine 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 candidates 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

• Work closely with Data Engineers, Software Engineers, MLOps teams, Product teams, and business stakeholders.
• Clearly communicate technical findings and modelling trade-offs.
• Translate complex scientific concepts into understandable recommendations for non-technical stakeholders.
• Collaborate effectively in cross-functional and Agile environments.

Leadership & Mentoring

For experienced candidates:

• Provide scientific leadership within the squad.
• Review modelling approaches and scientific work.
• Mentor Applied Scientists and junior ML practitioners.
• Establish and promote strong experimentation and modelling practices.
• Contribute to the overall applied research and machine learning standards of the team.

For junior candidates, prior mentoring or leadership experience is not mandatory.

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.

---