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Data Scientist

Remote · USA Full-time New today

Inspira is seeking highly skilled and intellectually curious Data Scientist to join our dynamic Data Science & Analytics Organization. The role is central to driving data informed decision-making across the organization by developing predictive models, uncovering insights and building scalable analytic solutions.

The ideal candidate will possess a strong foundation in statistical modeling, machine learning, and data engineering, along with the ability to communicate complex findings in a clear and actionable manner to both technical and non-technical stakeholders.

Duties & Responsibilities:

  • Develop and deploy predictive models and machine learning algorithms to solve business problems and optimize performance
  • Conduct exploratory analysis to identify trends, patterns and opportunities
  • Collaborate with cross-functional teams to define business questions and translate them into analytical frameworks
  • Build and maintain data pipelines and ensure data quality, integrity, and accessibility
  • Design and deliver visualizations and dashboards that communicate insights effectively
  • Apply statistical techniques to test hypotheses validate model performance
  • Document methodologies, code and findings to ensure transparency and reproducibility
Qualifications
  • Bachelors degree in Data Science, Statistics, Mathematics or related field (Masters degree preferred)
  • 3 – 5+ years of experience in data science or advanced analytics role
  • Proficiency in programming languages such as Python, SQL and R
  • Proficiency with statistical modeling and machine learning
  • Familiarity with cloud data platforms (Snowflake, Azure)
  • Experience with data visualization tools (Tableau, Looker)
  • Experience with Docker Containers
  • Excellent communication and storytelling skills
  • Experience with MLOps, model deployment, and version control (Git) [preferred]
  • Knowledge of natural language processing (NLP) or deep learning techniques
  • Exposure to semantic layer design and data governance frameworks

Originally posted on Himalayas

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