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Vice President, Machine Learning Engineering

Posted 2024-11-14

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💎 Seniority level: Vp, 8+ years industry experience and 2+ years in engineering management

📍 Location: United States of America

💸 Salary: 220000 - 280000 USD per year

🔍 Industry: Life Sciences and Healthcare

🏢 Company: Clarivate_Careers

⏳ Experience: 8+ years industry experience and 2+ years in engineering management

🪄 Skills: LeadershipProject ManagementPythonSoftware DevelopmentAgileArtificial IntelligenceData AnalysisMachine LearningPeople ManagementProduct ManagementSCRUMProject CoordinationCross-functional Team LeadershipProduct DevelopmentStrategyAlgorithmsData analysisData scienceGoCommunication SkillsAnalytical SkillsCollaborationProblem SolvingAgile methodologiesMentoringPresentation skillsTime ManagementWritten communicationDocumentationCoaching

Requirements:
  • Bachelor’s degree in Computer Science or related field (or equivalent applicable experience).
  • A seasoned technical leader, with 8+ years industry experience and 2+ years in engineering management.
  • Experience in delivering working ML/AI products.
  • Master’s or Ph.D. in Computer Science, Machine Learning, AI, or related field.
  • Experience collaborating with cross functional teams and product owners.
  • Strong understanding of machine learning algorithms, data preprocessing, and model evaluation.
  • Experience designing software for the life science industry.
Responsibilities:
  • Work with the SVP of AI Product Transformation to identify highest leverage AI implementation opportunities across the Clarivate life sciences portfolio.
  • Assist in communicating AI investment and growth opportunities to the ELT, CEO, and potentially Board of Directors.
  • Formulate, communicate, and lead iteration of implementation approaches to achieve our AI project goals.
  • Hire, lead and manage a diverse, high-performing engineering team to execute against these goals.
  • Work closely with individual product owners and biologists to understand workflows, constraints, business value and opportunities at a granular level.
  • Spend extensive time 'looking at data'.
  • Contribute meaningful amounts of code and/or documentation.
  • Mentor and develop a team of more junior, high performance, impact-oriented engineers and data scientists.
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