Middle Applied Scientist

N-iX is for a Middle Applied Scientist to join our team and improve how customers discover, evaluate, and select content for AI training datasets. You will own scoped scientific projects from problem framing through recommendation, while helping shape broader methods and standards with senior and principal data science colleagues. Working with product and engineering partners, you will turn evaluation results and experimental evidence into improvements to the Data Licensing platform.
This role is well suited to someone who has applied statistical and machine-learning methods in production and is ready to take greater ownership of product-facing scientific work.

Our customer is a technology company that powers one of the world's largest two-sided marketplaces for high-quality photos, illustrations, videos and music used by individuals, businesses, marketing agencies and media organizations of all sizes. In this role, you will be working with highly motivated and talented data scientists and engineers who are primarily focused on developing next-generation AI/ML systems that transform the way people find content they enjoy.

Responsibilities:

• Partner with senior and principal data scientists to improve dataset discovery, qualification, and selection by evaluating and enhancing retrieval, classification, filtering, and ranking approaches
• Develop and apply evaluation and sampling methods to measure dataset relevance, coverage, diversity, composition, and representativeness.
• Evaluate automated dataset-curation workflows, using structured error analysis to assess interpretation, selection quality, and failure modes and recommend improvements.
• Design and analyse experiments using offline evaluation and product telemetry to recommend improvements to curation quality, workflow performance, and customer experience.

Requirements:

• 4+ years of commercial experience in data science, applied science, machine learning, or a related field.
• Strong grounding in statistics, experimental design, and quantitative problem-solving.
• Experience evaluating search, recommendation, ranking, and similar machine-learning systems.
• Experience taking experiments from problem framing and metric definition through analysis and recommendation.
• Proficiency in Python and SQL and experience working with large datasets.
• Practical experience applying several relevant techniques, such as sampling, clustering, classification, optimisation, uncertainty estimation, or Bayesian methods.
• Experience using structured error analysis to identify opportunities to improve model or platform performance.
• Strong communication skills and experience working with product and engineering partners.
• Ability to innovate and identify new approaches to improve current practices.
• English level - upper-intermediate or higher
• Working experience AWS and/or GCP