Data Scientist

Role Overview

As a Data Scientist at Thermo Fisher Scientific, you will design, develop, and maintain scalable data pipelines and data processing solutions using Python, SQL, and PySpark in AWS environments. You will work with cross-functional teams to translate business requirements into data science solutions and contribute to the deployment and monitoring of models using MLOps best practices.

What You Will Do

Your key responsibilities will include designing and developing data pipelines, building and deploying machine learning and AI models, applying Generative AI techniques, and collaborating with cross-functional teams.

Why It Might Be a Fit

This role requires strong programming skills in Python, experience with PySpark, and hands-on experience with AWS services. You should have practical experience with machine learning frameworks and experience or exposure to Generative AI.

Requirements

• 3–5 years of experience in data science, machine learning, or applied AI
• Strong programming skills in Python and experience with PySpark for large-scale data processing
• Hands-on experience with AWS services (e.g., S3, Lambda, Glue, SageMaker, EMR)
• Practical experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
• Experience or exposure to Generative AI (LLMs, prompt engineering, vector databases, RAG pipelines)
• Strong knowledge of SQL and working with structured and unstructured data
• Understanding of data modeling, ETL processes, and big data architectures
• Experience with version control (e.g., Git) and collaborative development practices
• Strong analytical and problem-solving skills with the ability to work independently and manage multiple tasks