Stone Alliance Group Career Page

Post-Doctoral Fellow - Data Analytics, Innovation, and Rigor

New York City, New York · Full-time

Sponsorship not specified$60k-$79kDetected 22 hours ago
PythonMATLABData StructuresAlgorithmsGitDockerLinuxMachine LearningTensorFlowPyTorchscikit-learnData AnalysisData ScienceData VisualizationNLPControlsResearch

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odds of building a lasting career here

15Unrated
Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds (Level I)39
Fits your clock70

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About the role

  • Our client is seeking a Post-Doctoral Fellow in their Center for Data Analytics, Innovation, and Rigor (DAIR Center) in New York City.
  • Our client is dedicated to transforming the lives of children and families struggling with mental health and learning disorders by giving them the help they need.
  • This exempt, full-time, hybrid position requires a minimum of four (4) days per week in the office, on a schedule determined by your supervisor, in their New York City office.

Responsibilities

  • Lead the design and execution of research studies involving complex analytical workflows including data preprocessing, feature engineering, model development, scientific analyses, data visualization, and validation.
  • Develop reproducible, scalable, well-documented code and pipelines suitable for high-performance computing ecosystems, with version control through GitHub or equivalent repositories.
  • Collaborate with interdisciplinary research teams to define project objectives, establish timelines, monitor progress, and ensure successful completion of research milestones while contributing to the intellectual direction of the team through participation in research planning meetings and strategic discussions.
  • Perform additional job-related duties as assigned.

Compensation

  • $60k-$79k

Benefits

  • Apply both pre-trained and custom-made machine learning models and data manipulation pipelines to research and clinical contexts, ensuring the responsible handling of sensitive data.
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