Entry Level Data Scientist

About SideRamp

SideRamp allows professionals to engage with part-time opportunities in the gig economy, offering a centralized platform that manages high-quality, flexible side gigs. We focus on connecting our users to various freelance and part-time work—from discovering opportunities to streamlining the application process.

The Job Overview

We are looking for a Machine Learning Specialist to join a leading healthcare organization. As a Machine Learning Specialist, you will be responsible for developing and implementing machine learning algorithms to extract insights from complex data sets. Your role will involve working closely with data scientists and engineers to design and deploy machine learning models that drive business decisions and improve operational efficiency.

Responsibilities

• Develop and implement machine learning algorithms to solve complex business problems
• Collaborate with cross-functional teams to design and deploy machine learning models
• Analyze and interpret large data sets to extract meaningful insights
• Evaluate the performance of machine learning models and make recommendations for improvements
• Stay up-to-date with the latest trends and advancements in machine learning and artificial intelligence
• Communicate findings and insights to stakeholders in a clear and concise manner
  
  

Job Qualifications And Skill Sets

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field
• Proven experience in developing and deploying machine learning models
• Strong programming skills in languages such as Python, R, or Java
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn
• Solid understanding of statistics, data structures, and algorithms
• Excellent problem-solving and critical thinking abilities
• Ability to work independently and in a team environment
  
  

Note: Job descriptions may have been edited from their original format to ensure fair hiring standards along with consistent formats across all postings. Salary information is not guaranteed and many estimates are based on a yearly, full-time salary. This system can make mistakes.