Jobs · Engineering · Missouri

Data scientist/data modeler/AI/ML

MACHINE LEARNING TECHNOLOGIES LLC · St Louis, MO · 2 wk ago
EngineeringContract

About the role

A Developer Advocate at Global Data Assets & Analytics (GDA&A) for the Decision Science Ecosystem platform serves as the bridge between the DSE platform team and the data science community. This role focuses on enabling data scientists to successfully model and operationalize their solutions on the DSE platform.

Responsibilities

  • Partner with member of the Data Science community to implement best practices and align with platform vision
  • Develop and contribute tools that simplify platform operations
  • Create educational content and provide technical support
  • Gather developer feedback and advocate for community needs
  • Foster a thriving data science community

Requirements

  • Masters degree
  • Data scientist, data modeler, artificial intelligence (AI)
  • Data science: Python or R, in libraries like pandas, NumPy, scikit-learn, and data visualization tools
  • Top cloud : AWS sagemaker (streamline machine learning (ML) from data preparation to model deployment), if not AWS then GCP, Azure.
  • MLOps practices, and model deployment strategies

Qualifications

  • Perfect Communication
  • Foster an developer community around Global Data Assets & Analytics platforms and technologies.
  • Participate in and organize hackathons, workshops, and meetups to facilitate interaction and knowledge sharing, aligned with platform’s roadmaps.
  • Build relationships with key influencers, community leaders, and other organizations to extend the platform’s reach and amplify its messages.
  • Advocate for 600+ individuals in the data realm.
  • Need to read code but will not be developing code.
  • Evangelism and Promotion

Skills

  • Data scientist, data modeler, artificial intelligence (AI)
  • Data science: Python or R, in libraries like pandas, NumPy, scikit-learn, and data visualization tools
  • Top cloud : AWS sagemaker (streamline machine learning (ML) from data preparation to model deployment), if not AWS then GCP, Azure.
  • MLOps practices, and model deployment strategies

Pay

Depending on Experience (DOE)

Schedule

12 Months + Extension

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