Applied Scientist / Applied AI Scientist / Principal Applied Scientist

Role DescriptionApplies scientific methods, machine learning, and statistical analysis to solve practical problems and improve products, services, or business processes. Works with product managers, engineers, data scientists, and subject matter experts to define problems, identify useful data, develop hypotheses, and evaluate potential approaches. Connects research findings to systems or features that can deliver measurable value in real-world settings.An Applied Scientist conducts experiments, develops models or algorithms, evaluates results, and collaborates with engineering teams to implement successful approaches. An Applied AI Scientist focuses specifically on artificial intelligence methods, which may include machine learning, deep learning, natural language processing, computer vision, generative AI, or other techniques relevant to the product. A Principal Applied Scientist sets technical direction for complex or high-impact projects, guides research and development across teams, mentors scientists, and advises senior leaders on scientific and technical trade-offs.Responsibilities may include preparing and analyzing datasets, selecting modeling approaches, designing experiments, building prototypes, and comparing methods using appropriate measures. Tests models for accuracy, robustness, efficiency, and suitability for the intended use. Investigates errors, bias, data limitations, and performance changes, and documents assumptions and limitations. Works with software and platform engineers to deploy models, integrate them into products, and monitor their behavior after release.Communicates results through technical reports, presentations, and product recommendations. Helps translate broad business or user needs into well-defined scientific questions and practical implementation plans. May contribute to research publications, patents, technical standards, or open-source work, depending on the organization. Principal-level roles may also establish research priorities, review technical approaches, coordinate work across multiple teams, and ensure that solutions meet quality, privacy, safety, and reliability expectations.QualificationsMaster’s degree or PhD in computer science, machine learning, statistics, applied mathematics, engineering, or a related field is often preferred; equivalent applied experience may be consideredExperience in applied research, machine learning, artificial intelligence, data science, or a related technical disciplineStrong foundation in statistics, experimental design, optimization, and model evaluationProficiency in Python and common scientific computing or machine learning frameworksAbility to prepare, analyze, and validate large or complex datasetsExperience developing prototypes and evaluating models against clear objectives and baselinesFamiliarity with production machine learning, model deployment, experimentation, or monitoring is beneficialStrong problem-solving skills and ability to assess trade-offs among performance, complexity, cost, and reliabilityClear written and verbal communication skills for technical and non-technical audiencesAbility to collaborate with product, engineering, data, and business stakeholdersAttention to data quality, reproducibility, model limitations, and responsible AI considerationsPublications, patents, open-source contributions, or demonstrated applied research are an assetPrincipal-level positions generally require a record of leading high-impact technical work, mentoring colleagues, and influencing scientific or product directionRelevant subject-matter expertise in the organization’s industry or application area is beneficial