Data Science Co-op: AI for Critical Care R&D

Johnson & Johnson MedTech; Danvers (MA); On-site; USD 34,000 - 73,000; Full time

Job summary

Johnson & Johnson is seeking a Data Science Co-op to join the Critical Care team (Applied Sciences & AI) in Danvers, MA. The role involves solving data science problems, building ML tools, and researching new applications within the company.

You will implement ML for algorithms, design products, and translate algorithms for internal analysis tools, working with a strong emphasis on data interpretation and collaboration with the Engineering team.

Qualifications

• Senior or grad student pursuing a degree in data science, computer science, computer engineering, software engineering, AI, or statistics.
• Strong Machine Learning background with experience in Deep Learning (Tensorflow, Keras, Pytorch).
• Proficient in at least one programming language (Python preferred) with solid understanding of data structures & algorithms.
• Solid understanding of statistics and strong analytical skills.
• Self-motivated and able to execute tasks in a timely manner.
• Excellent written and verbal communication skills.

Responsibilities

• Assist in designing and training machine learning models for Clinical Decision Support.
• Provide input into process development and improvement.
• Analyze data and present results to the larger Engineering team.
• Contribute to research in new data science applications within the company.

Skills

Machine learning

Deep learning

Time series modeling

NLP

LLMs

Communication

Self-motivation

Analytical skills

Education

Pursuing degree in data science or related field

Tools

TensorFlow

Keras

PyTorch

NumPy

Pandas

Matplotlib

Scikit-learn

NLTK

Job description

Johnson & Johnson is seeking a Data Science Co-op to join the Critical Care team (Applied Sciences & AI) in Danvers, MA. The role involves solving data science problems, building ML tools, and researching new applications within the company.

You will implement ML for algorithms, design products, and translate algorithms for internal analysis tools, working with a strong emphasis on data interpretation and collaboration with the Engineering team.