Contribuer à la création d’un agent IA dédié à l’aide sur les flots de conception numérique et analogique.
Responsibilities
Concevoir un agent IA d'assistance à conception par ordinateur des puces microélectroniques à partir de la documentation technique et des données du système de support
Collaborer avec les experts techniques pour sélectionner les informations pertinentes à intégrer
Mettre en place un jeu de tests pour valider la qualité des réponses et améliorer l’agent en continu
Proposer des solutions innovantes pour améliorer l’usage de l’IA dans l’assistance aux activités de conception
Collaborer avec les autres personnes en charge de sujets similaires, appliqués à d’autres métiers, afin de partager les bonnes pratiques, mutualiser les approches et enrichir l’agent
Requirements
Bachelor's in Microelectronics or Computer Science
Lead R&D work in artificial intelligence for formal verification within the VITAMIN-C project.
Compensation
Indicative remuneration range (excluding the variable annual bonus): €33,614 – €43,000 gross per annum, depending on profile and experience, as defined by our legal framework.
Responsibilities
Carry out R&D work in artificial intelligence for formal verification
Contribute to the research profile of Télécom Paris and Institut Mines-Télécom
Requirements
Bac+5 or equivalent
Bac+4 with at least four years’ professional experience
The consultant will develop AI and ML solutions, support technical aspects of pilot delivery to catalyze impact at WFP, assess/communicate technical value to stakeholders, and scout for collaborations with AI thought leaders.
Responsibilities
Drive the responsible development of selected AI & ML based software solutions from ideation to pilot implementation (Technology Readiness Level 5-6) via the WFP AI Sandbox
Use available tools and expertise to develop lightweight technology prototypes to test research hypotheses
Develop architecture standards, templates, and principles for WFP Innovation Accelerator AI projects
Map solution design to WFP Enterprise Architecture standards and referencearchitectures
Collaborate with all necessary stakeholders, including business and IT roles, to gather needed input to derive agreed solution
The AI engineer is responsible for developing and implementing artificial intelligence solutions to solve complex problems and enhance business operations.
Lead the evolution of Cardinal Health’s Advanced Practice Analytics platform, driving AI‑first financial intelligence and reimbursement analytics for specialty practice profitability.
Compensation
Anticipated salary range: $96,300 - $137,600
Responsibilities
Lead Product Strategy & Roadmap
Optimize Practice Economics & Revenue Cycle Management (RCM)
Drive Platform & Dataset Synergies
Execute with Product Excellence
Amplify the Voice of the Customer (VOC)
Requirements
Bachelor’s degree in Computer Science, Business, Healthcare Administration, or equivalent practical experience
Nice to have: specialty therapeutic area exposure (oncology, urology, rheumatology, gastroenterology)
Lead end‑to‑end design, development, and deployment of advanced machine learning models and AI‑driven products in healthcare.
Responsibilities
Lead the end‑to‑end design, development, and deployment of advanced machine learning models and AI‑driven products that deliver significant business value.
Act as the technical lead on complex data science projects, defining the roadmap, setting priorities, and ensuring the scientific rigor of the team's work.
Architect and implement scalable, production‑ready data science pipelines and MLOps frameworks in collaboration with engineering teams.
Serve as a primary consultant to executive leadership and business stakeholders, translating complex data insights into strategic recommendations.
Mentor and develop other data scientists and engineers, fostering a culture of innovation, collaboration, and continuous technical growth.
Requirements
Master’s degree or PhD in a quantitative field such as Computer Science, Data Science, Statistics, Physics, or a related discipline
As a Data Scientist you will modernise analytics infrastructure and deliver actionable insights across the Government Commercial Function and Government Grants Management Function, empowering leaders and citizens to guide public spending.
Locations (2)
Liverpool, England, United Kingdom
Birmingham, England, United Kingdom
Compensation
Salary: £48,027 - £54,751 (plus allowance up to £6,000 per annum depending on technical assessment)
Responsibilities
Project Leadership: leading data science projects
Analysis & Insights: scoping and leading proactive analysis and technical tasks
Data Services & Pipelines: working with digital team to map, produce, transform and test new data services
Analytics Products: developing new analytics products to provide deeper insights
Technical Expertise & Innovation: providing technical expertise on data science techniques
Lead independent validation and challenge of AI/ML and generative AI models across Vanguard’s enterprise, ensuring sound governance and risk compliance.
Responsibilities
Assess models and systems using generative AI throughout their lifecycle
Provide effective challenge of model assumptions, methodologies, data, testing, implementation, monitoring and limitations
Assess compliance with internal AI standards and regulatory requirements
Ensure timely execution of model validation and high‑quality reports
Develop, maintain and enhance validation procedures
Requirements
PhD in a quantitative discipline such as Computer Science, Mathematics, Statistics, Physics, Engineering or equivalent
Develop advanced machine learning solutions for content understanding across audio, text, and multimodal sources.
Responsibilities
Design, develop, deploy, and improve machine learning models that support content understanding across audio, text, and multimodal content sources.
Apply modern machine learning techniques, including deep learning, transformer architectures, large language models, and multimodal learning approaches to solve content understanding challenges.
Own complex machine learning projects from problem definition through model development, deployment, evaluation, and ongoing optimization.
Evaluate model performance using appropriate offline metrics, experimentation frameworks, and business outcomes to drive continuous improvement.
Collaborate closely with Product, Engineering, Analytics, and Data teams to identify opportunities and deliver impactful machine learning solutions.
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