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Trainee Data Scientist

Sajix Inc.Pleasanton
On-site Intern

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Experience Level

Entry Level

Qualifications

Requirements:A Bachelor’s or Master’s degree in Computer Science, Statistics, or Data Science. Basic proficiency in Python or R, along with familiarity with libraries such as Pandas, Scikit-learn, or NumPy. A foundational understanding of statistics, probability, and the basics of machine learning.

About the job

About the Role:
Join Sajix as a Trainee Data Scientist, where you will play a pivotal role in developing predictive models and conducting experiments to extract valuable insights from intricate healthcare data.

Key Responsibilities:

  • Assist in the cleaning and preprocessing of both structured and unstructured data to ensure high-quality datasets.

  • Support the creation and refinement of statistical and machine learning models to enhance data-driven decision-making.

  • Engage in exploratory data analysis and feature engineering to uncover hidden patterns and improve model performance.

  • Document your findings and effectively communicate insights to non-technical stakeholders, promoting data literacy within the organization.

About Sajix Inc.

Sajix Inc. is an innovative health-tech organization based in Pleasanton, California. Established in 2006, we are dedicated to revolutionizing healthcare delivery through cutting-edge digital solutions. Our expertise lies in developing integrated healthcare information systems that enhance clinical, financial, and administrative processes for healthcare institutions globally. Operating across the United States, United Kingdom, Singapore, and India, Sajix caters to clients throughout North America, Europe, Asia, and Africa. Our flagship product, iHelix, is a modular and scalable platform suitable for diverse healthcare environments, from small practices to extensive hospital networks. At Sajix, we offer a wide range of digital healthcare solutions, including:iHelixLifeeazyAI-driven Revenue Cycle Management – optimizing financial workflows and enhancing operational efficiency.

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