Data Scientist

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Department: AST Services

Location: Gainesville, FL

Description

The Data Scientist is responsible for training and validating supervised and unsupervised machine learning models to predict outcomes of joint arthroplasty. They will also conduct advanced statistical analysis, including clustering and fairness and bias assessments, to evaluate ML model performance. Additionally, the Data Scientist will prepare documentation to support the regulatory team with FDA submissions.

Key Responsibilities

  • Collaborate with cross-functional stakeholders, including product and service teams, to define research questions and execute research initiatives related to joint arthroplasty outcome prediction.
  • Designs, develops, and validates relevant supervised and unsupervised ML algorithms to analyze structured and unstructured large datasets from multiple disparate sources and train predictive models.
  • Perform data exploration (e.g., clustering) and identify meaningful insights from large data and metadata sources; conduct fairness and bias analyses to evaluate model performance; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers.
  • Apply principles of Responsible AI to ensure models are interpretable, robust, and ethically sound.
  • Conduct reproducible studies with well-maintained professional documentation; ensure proper organization of data and code repositories.
  • Contribute to the preparation of progress reports and presentations for internal and external stakeholders, including the regulatory team.
  • Publish research findings in peer-reviewed journals and present at scientific conferences.
  • Know and apply the Quality System and relevant Federal and International standards.

Skills Knowledge and Expertise

Education:
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field from an accredited institution.
Experience:
  • Candidates with a Bachelor’s degree are preferred to have at least two years of relevant work experience.
  • Having conference or journal publications related to AI/ML or data processing is a plus.
Functional/Technical Knowledge, Skills and Abilities Required:
  • Strong foundation in technical principles and practices including: data cleaning and preprocessing (structured and unstructured data), machine learning, and training/validation of classification and regression models.
  • Proficiency in Python, with experience using libraries such as Pandas, XGBoost, and Scikit-learn.
  • Familiarity with deep learning frameworks like TensorFlow or PyTorch is a plus.
  • Experience with building machine learning pipelines on cloud platforms such as Microsoft Azure, Google Cloud, or AWS is preferred.
  • Proficiency in using Git and GitHub for version control and team collaboration.
  • Excellent communication (verbal & written), organizational, and interpersonal skills, with the ability to thrive in a remote, collaborative team environment.
  • Adherence to the Quality System and relevant Federal and International standards.
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