Senior Data Scientist

Overview

The Emmes Company, LLC (“Emmes”) is a global, full-service Clinical Research Organization dedicated to excellence in supporting the advancement of public health and biopharmaceutical innovation. We believe in the power of truth, so much so that we named our company Emmes, which means truth. Through decades of experience we have learned that collaborative relationships thrive and human health benefits when truth is our compass.

Our “Character Achieves Results” culture is driven by five key values that guide our actions in the way we conduct research and distinguish us as an organization: Integrity, Agility, Passion for Excellence, Collaborative Partnerships and Intellectual Curiosity. We are a trusted partner to clients who share our passion for improving public health in a world of ever-changing scientific research.

If you share our motivations and passion in research, come join us! You will be joining a collaborative culture that empowers every Emmes employee — from entry level through top executive — to contribute to our clients’ success by sharing ideas openly and honestly.

Primary Purpose

We are seeking a talented and motivated Data Scientist to join our team and play a key role in developing innovative AI solutions using the AWS technology stack. You will be responsible for developing, assessing, and fine-tuning various AI and machine learning models and you will oversee training, testing, and deployment of these models. You will work closely with cross-functional teams, including, data engineers, AI engineers, software engineers, and product managers, to bring AI-powered solutions to life and drive clinical trial acceleration and insights.

Responsibilities

AI Model Development and Deployment:

• Design, build, and deploy state-of-the-art AI models for diverse use cases (e.g., natural language processing, recommender systems, generative AI using deep learning and large language models (LLMs)).
• Select and implement appropriate deep learning, reinforcement learning, or other AI techniques based on the problem and data characteristics.
• Train and evaluate models on the AWS platform using services like Amazon SageMaker and Machine Learning (ML) Compute.
• Help operationalize models into production systems for real-time prediction and decision-making.