Machine Learning Scientist / Applied Scientist / Research Scientist

Role Description

Advance scientific and technical capabilities through research, modelling, and rigorous experimentation. Develop methods that address research questions or practical problems, producing reproducible evidence and collaborating with engineering and domain specialists to translate findings into useful outcomes.

Key Responsibilities

• Review relevant literature, identify knowledge gaps, and formulate research questions.
• Define hypotheses, baselines, evaluation criteria, and experimental methods.
• Gather and assess data for quality, representativeness, provenance, and permitted use.
• Develop models, algorithms, or analytical methods appropriate to the problem.
• Conduct controlled experiments and evaluate results with suitable statistical rigor.
• Investigate errors, robustness, uncertainty, and limitations.
• Maintain reproducible code, configurations, and research records.
• Assess computational efficiency and practical implementation constraints.
• Collaborate with researchers, engineers, product teams, and domain experts.
• Consider safety, privacy, security, and broader impacts relevant to the work.
• Communicate findings through technical reports, presentations, publications, or other approved outputs.
• Refine research priorities as evidence and project needs evolve.

Role-Specific Responsibilities

• Machine Learning Scientist:

Develop and evaluate learning algorithms, model architectures, training techniques, and generalization methods.

• Applied Scientist:

Adapt or develop scientific methods to solve practical problems, balancing rigor with operational constraints and measurable user or business outcomes.

• Research Scientist:

Lead original investigations within a defined discipline, develop novel insights, and contribute to the organization’s scientific direction.

Qualifications

• Strong grounding in the scientific methods, mathematics, and statistics relevant to the role.
• Proficiency in Python or another appropriate research programming language.
• Experience designing experiments, assessing evidence, and interpreting uncertainty.
• Ability to critically evaluate literature and implement relevant methods.
• Strong analytical, problem-solving, and technical communication skills.
• Familiarity with version control, testing, and reproducible research workflows.
• A postgraduate degree in computer science, mathematics, statistics, engineering, or another relevant discipline, or equivalent demonstrated research experience.
• Machine Learning Scientist:

Strong understanding of machine learning fundamentals and experience with relevant modelling frameworks.

• Applied Scientist:

Evidence of translating scientific methods into validated solutions for real-world problems.

• Research Scientist:

A PhD or equivalent record of independent, original research appropriate to the discipline and seniority.

Preferred Qualifications

• Publications, open-source contributions, or other evidence of scientific impact.
• Expertise in a relevant specialty, such as language processing, computer vision, optimization, causal inference, or reinforcement learning.
• Experience with large datasets, distributed experimentation, or accelerator computing.
• Knowledge of interpretability, robustness, uncertainty estimation, or advanced evaluation methods.
• Experience transferring research into products or operational use.
• Ability to collaborate across disciplines and mentor colleagues.

Scope by Role

• Machine Learning Scientist:

Focuses on developing and validating machine learning methods.

• Applied Scientist:

Connects scientific investigation with practical solution development.

• Research Scientist:

Focuses on original contributions and research direction, with subject area and independence determined by the position.